{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Yellowbrick是一个机器学习可视化库,主要依赖于sklearn机器学习库，能够提供多种机器学习算法的可视化，主要包括特征可视化，分类可视化，回归可视化，回归可视化，聚类可视化，模型选择可视化，目标可视化，文字可视化。本节主要介绍Yellowbrick如何快速使用。\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 1 使用说明\n",
    "## 1.1 背景介绍\n",
    "Yellowbrick有两个主要依赖项：scikit-learn和matplotlib。如果您没有这些Python软件包，它们将与Yellowbrick一起安装。请注意，Yellowbrick在scikit-learn 0.20或更高版本和matplotlib 3.0.1或更高版本中效果最佳。这两个软件包都需要编译一些C代码，这在某些系统（例如Windows）上可能很难。如果遇到问题，请尝试使用包含这些程序包的Python发行版（如Anaconda）。\n",
    "\n",
    "Yellowbrick也通常在Jupyter Notebook与Pandas一起使用。笔Notebook使协调代码和可视化变得特别容易。但是，您也可以在常规Python脚本中使用Yellowbrick，将图形保存到磁盘或在GUI窗口中显示图形。如果您对此有疑问，请查阅matplotlib的后端文档。\n",
    "\n",
    "Yellowbrick是Pytho 3软件包，可与3.4或更高版本一起使用。最简单的安装Yellowbrick的方法是从PyPI使用Python首选的软件包安装程序pip。如下所示：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pip install -U yellowbrick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.2 Yellowbrick简单说明\n",
    "**本部分介绍Yellowbrick基本说明，可以跳过。**\n",
    "\n",
    "Yellowbrick API是专门为与scikit-learn配合使用而专门设计的。因此，主要界面是Visualizer–一个从数据中学习以产生可视化效果的对象。可视化工具是scikit-learn的Estimator对象，具有类似的界面以及绘图方法。为了使用可视化工具，您只需使用与scikit-learn模型相同的工作流程，导入可视化工具，实例化它，调用可视化工具的fit()方法，然后为了渲染可视化效果，调用可视化工具的show()方法。\n",
    "例如，有几个可视化工具充当转换器，用于在拟合模型之前执行特征分析。以下示例显示具有平行坐标的高维数据集：\n",
    "```\n",
    "from yellowbrick.features import ParallelCoordinates\n",
    "\n",
    "visualizer = ParallelCoordinates()\n",
    "visualizer.fit_transform(X, y)\n",
    "visualizer.show()\n",
    "```\n",
    "\n",
    "如您所见，工作流程与使用scikit-learn转换器非常相似，并且可视化工具旨在与scikit-learn实用程序集成在一起。可以在实例化时将更改绘制可视化方式的参数传递给可视化器，这与scikit-learn模型如何包含超参数类似。\n",
    "\n",
    "该show()方法最终确定图形（添加标题，轴标签等），然后代表您渲染图像。如果您在Jupyter笔记本中，则图像应仅出现在笔记本输出中。如果您使用的是Python脚本，则应打开GUI窗口，并以交互形式显示可视化内容。但是，您还可以通过传递文件路径来将映像保存到磁盘，如下所示：\n",
    "```\n",
    "visualizer.show(outpath=\"pcoords.png\")\n",
    "```\n",
    "\n",
    "文件名的扩展名将决定图像的呈现方式。除.png扩展名外，.pdf还通常用于准备高质量出版物的图像。\n",
    "\n",
    "另外输入到Yellowbrick的数据与scikit-learn的数据相同。通常使用变量X（有时简称为数据）和可选变量y（通常称为目标）来描述数据集。所需数据X是一个表，其中包含由功能部件描述的实例（或样本）。X因此是一个二维矩阵，其形状为，其中是实例数（行）和要素数（列）的数量。可以是Pandas DataFrame，NumPy数组，甚至是Python列表。\n",
    "可选目标数据，y用于指定监督机器学习中的标签。y是一个向量（一维数组），向量的长度n与X中的行数相同。y可以是Pandas series，Numpy数组或Python列表。\n",
    "\n",
    "可视化工具还可以包装scikit学习模型以进行评估，超参数调整和算法选择。例如，要生成分类报告的可视化热图，以显示分类器中的每个分类的精度，召回率，F1得分和支持，请将估计器包装在可视化器中，如下所示：\n",
    "```\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "visualizer = ClassificationReport(model)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()\n",
    "```\n",
    "添加分类器模型的视觉评估，添加ClassificationReport包装分类估计器的可视化器实例化以及对其show()方法的调用，只需两行代码即可。这样，可视化工具可以在不中断机器学习的情况下增强其工作效率。\n",
    "\n",
    "基于类的API旨在直接与scikit-learn集成，但是有时有时您只需要快速可视化即可。Yellowbrick支持快速功能以直接利用此功能。例如，两个视觉诊断原本可以按以下方式实现：\n",
    "```\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from yellowbrick.features import parallel_coordinates\n",
    "from yellowbrick.classifier import classification_report\n",
    "\n",
    "# Displays parallel coordinates\n",
    "g = parallel_coordinates(X, y)\n",
    "\n",
    "# Displays classification report\n",
    "g = classification_report(LogisticRegression(), X, \n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.3 演练\n",
    "让我们考虑将回归分析作为在机器学习工作流程中使用可视化工具的简单示例。使用基于共享到[UCI机器学习存储库](https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset)的自行车共享数据集，我们希望根据季节，天气或假期等功能，预测给定小时内租用的自行车数量。\n",
    "\n",
    "pandas模块已经包含这个数据，我们可以使用yellowbrick.datasets模块更快加载数据，并且打印前几行数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   season  year  month  hour  holiday  weekday  workingday  weather  temp  \\\n",
      "0       1     0      1     0        0        6           0        1  0.24   \n",
      "1       1     0      1     1        0        6           0        1  0.22   \n",
      "2       1     0      1     2        0        6           0        1  0.22   \n",
      "3       1     0      1     3        0        6           0        1  0.24   \n",
      "4       1     0      1     4        0        6           0        1  0.24   \n",
      "\n",
      "   feelslike  humidity  windspeed  \n",
      "0     0.2879      0.81        0.0  \n",
      "1     0.2727      0.80        0.0  \n",
      "2     0.2727      0.80        0.0  \n",
      "3     0.2879      0.75        0.0  \n",
      "4     0.2879      0.75        0.0  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\r\n",
    "from yellowbrick.datasets import load_bikeshare\r\n",
    "\r\n",
    "X, y = load_bikeshare()\r\n",
    "print(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "机器学习工作流程是创建模型选择三元素的艺术，模型选择三元素是特征、算法和超参数的组合，这些特征、算法和超参数唯一地标识了适合于特定数据集的模型。作为特征选择的一部分，我们希望识别彼此具有线性关系的特征，可能会在我们的模型中引入协方差并破坏OLS（指导我们移除特征或使用正则化）。我们可以使用Rank Features visualizer计算所有特征对之间的Pearson相关性，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ecf09b090>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.features import Rank2D\r\n",
    "\r\n",
    "# 创建特征相关性排名的2D图像\r\n",
    "visualizer = Rank2D(algorithm=\"pearson\")\r\n",
    "# 转换数据\r\n",
    "visualizer.fit_transform(X)\r\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "以上结果向我们显示了特征对之间的皮尔逊相关性，这样网格中的每个像元都代表了两个特征，这些特征在x和y轴上按顺序标识，并且颜色显示了相关性的大小。皮尔逊相关系数为1.0表示变量对之间存在强的正线性关系，值-1.0表示强的负线性关系（零值表示无关系）。因此，我们正在寻找深红色和深蓝色框以进一步识别。\n",
    "\n",
    "在此图表中，我们看到这些特征temp与feelslike具有很强的相关性，并且season与month具有很强的相关性。这似乎是有道理的。我们在外面感觉到的表观温度取决于实际温度和其他空气质量因素，而一年中的季节用月份来表示！要深入研究，我们可以使用 直接数据可视化（JointPlotVisualizer）检查这些关系。\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ecf008210>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.features import JointPlotVisualizer\r\n",
    "\r\n",
    "visualizer = JointPlotVisualizer(columns=['temp', 'feelslike'])\r\n",
    "visualizer.fit_transform(X, y)\r\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "该可视化工具绘制了y轴上的表观温度feellike和x轴上的实际测量温度的散点图，并使用简单的线性回归绘制了一条最佳拟合线。此外，单变量分布在x轴上方显示为直方图，y轴上显示为feellike。\n",
    "\n",
    "JointPlotVisualizer可以让您一目了然地看到特性之间非常强的正相关关系，以及每个特性的范围和分布。请注意，轴标注化为0和1之间的空间，这是机器学习中的一种常用技术，用于减少一个特征对另一个特征的影响。\n",
    "\n",
    "该图非常有趣，因为数据集中似乎存在一些离群值。这些实例可能需要手动删除，以提高最终模型的质量，因为它们可能表示数据输入错误，并有可能在偏斜的数据集上训练模型，这将返回不可靠的模型预测。离群值的第一个实例出现在临时数据中，其feelslike 值大约等于0.25-显示了一条水平的数据线，可能是由输入错误造成的。\n",
    "\n",
    "我们还可以看到，越极端的温度对感知温度产生了夸张的影响；温度越低，人们就越可能相信它是冷的，温度越高，人们就会感觉到温度越高，一般来说，温和的温度对个人对舒适感的感知影响不大。这给我们提供了一个线索，feellike可能是一个比temp更好的特性，它保证了一个更稳定的数据集，并且运行到异常值或错误的风险更小。\n",
    "\n",
    "我们可以通过对任一值进行模型训练并对结果进行评分，最终确定假设。如果该temp 值确实不太可靠，则应删除该temp 变量，改为使用feelslike。同时，feelslike 由于没有异常值和输入错误，我们将使用该值。至此，我们可以训练模型了。让我们对模型拟合线性回归并绘制残差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rxGIxbr/9du6++26UUqxcuZL3v//9uzW/kMnnhlOXAbCmeQcdmTwzk/1ReSF7N1O1bu9TsdTN7WmWffWeCR1z3ScvZknjUEdwOZ/85CcB+O1vf8uGDRv4xCc+Me7xTz311HEf29TUxHXXXce73vUunn/+ea6++uqSYCry/e9/n8svv3zcY5bj+z5f/OIX+elPf8rs2bO59NJLOeOMM1i8eHHpmGg0yn//93/T2NiI67pcdtllnHrqqRx11FHs2LGDO+64gz/+8Y/E43FuvPFG7rvvPg477DDuvvtu7r77biKRCFdddRXLly9n4cKFuzXPkMnFNAw+fPqhfPCUpWEeU8iksE8Jpkrjt7/9LY8++ig7d+7klltu4Ytf/CKZTIZcLsdnPvMZjjjiiJIwO/jgg1m7di0dHR1s3ryZK6+8kpUrVw4Yz3Eczj77bAAWLFhAJBIp7VNK8Y1vfINTTz2VQw89dLfm+/zzz7Nw4UL2228/AC644AJWr149QDAJIUoJzZ7n4XkeQvQH5/i+Ty6Xw7Iscrkcs2bNorm5mSOOOIJEIgHA8ccfzwMPPMDVV1+9W/MMmRpilhkGOoRMCqFgmmZaWlq46667eOWVV1i5ciUrVqzgySef5Ic//CHf+c53Bhzb1NRUOvYjH/nIEMHU1NTEgQceiFKKX/ziF3z4wx8u7fv5z3/Ok08+SW9vL6+++irvete7Svsuu+yyYfOOPvGJT3DSSSeVXu/YsYM5c/qDbmbPns3zzz8/5Dzf97n44ot57bXXuOyyyzjyyCNLx19xxRUsX76cWCzGySefzCmnnEJzczPf+ta36OzsJB6P8+ijj1ZcC4aQkJCpIxRM08zhhx+OEIKGhga+973v8eMf/5hCoTBsGaWjjjoK0zSZM2cOvb29A/a1tLTQ19fHNddcw44dO7Btm+uvv760/73vfS/vfe97h53DL3/5ywm9J9M0ueeee+jp6eFDH/oQTU1NLFmyhO7ublavXs3q1auprq7mxhtv5J577uHiiy/mqquu4sorrySRSLB06VKMsIJAxeNLj7ybIRZJYhrhUhIycYSfpmmmaG67/fbbmT17NrfccgsvvPACX//614ccO1p5paamJo477jjuuOMOuru7ufDCC3nuuec45phjxpzDeDWm2bNns3379tLrHTt2MHv27BHHramp4cQTT+Sxxx5jyZIlPPHEEyxYsICZM2cCcPbZZ/Pcc89x8cUXs3LlypIGeNttt406bsjEUax3V/B1qafxCBupJOtef5ztPZvIuRnikSRzag5i2fxTMET4gyJkzwkFU4XQ2dlJMat61apVu1xGyXEcDjnkEABmzJjBhRdeyCOPPDIuwTRejenwww/nlVdeYcuWLcyePZv77ruPW2+9dcAxHR0dFAoFUqkUuVyOJ554ouQrmjdvHv/4xz/IZrPE43GefPLJksmuvb2d+vp6tm3bxgMPPMCvfvWrXbn9kF1kcL07y83yz9nNHNyQoeCNLmzWvf44r3WsQwiBaVi4foHXOtYBcOiC8QfrhISMRPjzpkK4+OKL+elPf8oVV1zBEUccQWtrK7/5zW/Gfb7jOCxb1h+ue8YZZ/DII49M6Bwty+Kzn/0sV111Feeffz7nnXceBx98MABXX301O3bsYOfOnVxzzTVcdNFFXHrppZx00kksX74cgCOPPJJzzjmHt73tbVx00UVIKXnHO3Tu3vXXX8/555/Ptddey+c+9zlqhil5EzJxfPvRdfz+pa2k8x4xy2TxrBa6s8280t45QNise31gzpsvPbb3bBoQ0AI66GV7zyZ8OTTxNiRkVxFqUKLcG5W1a9ceAGwerVbehOUxlfl/xpPHNJnsa7XyYNdrH1ZaXTqY3jnlPZ9/vv2RUvUGQ0hOmvcciZjANATLZs/Al4qIaRCzYpy+9N0ls14m38PD638xrJnPlz7Ll15OMjYxPyp2s1begccee+wrg8Y5gEFrQ0g/oz276WKfMuWZhjFmztFY9PWZFScIQvZddqcnUrHeXcQ0cH1JTcwlHvGACH15j5e2d6GURAiDuoTFCYU01fFaAGKRJPFIkryXx/UlEdMoVRmPRxJYZpRMvicMiAjZI8JPTkjIG5Dd6YlUFGKJiEFXrsCO3hyur4hbisMbTZJRn6ponmREIoTEV4L2Pov3/fIZ7n7/mcG4Bk5rkr78dlxfa1W18SjzZyTAgMea7goDIkL2mFAwhYS8QSjXjr73+Hp+/9JWDCGIWSbpvMfvX9oKwIdPH5hAXRRiqzdsZ2dvjr6Cx850Fl+BKcD1Bet2JjjlgA5SUR+FQCEwhMISEuQ6bnt4Dh8783C+/eg67n2phkMa6pld1YUlXFp6PZRyWVALhmGEAREhe0womEJCKhxfSm57+CUecFrozBZorIqztauPxurEgOMMIVjTvIMPnrKUmGWWBNnP/9bMj57aQHfOpeBJcp6PaQhSUf31d6XiidfqOXH/TqTUAkkqQcY16c5HOaguzUMbt/GvJ9usad6BEAbr2heyrn0BpiigsFh+wHoW1A30/RUDIpbKk0KzXsguEX5aQkIqGF9KLvnJw6zeuJ2Cp3ONIqaBlIp0weOAmVUAgVlN0JHJs7M3x6//8QprmnfQms7z0vZOUD51CYVUJtJVSAlZ1+eYBfX4UiG8DvKeRYtrYQrwldaaBJCKengyw8a2XlrTeYSAnb05unMFXF9Rn/Q4df8sBS9JPDLQz5Vzs+TdzIQFRITsG4SCKSRkktndCgl5z+dLDzzPqqYWXKkQQqCAvCdRwJauDK19eQRgGQYRUzC7Ks4vn93E/eu3YQiBEIo3L9zBslm9JCxFxo3w8s4ED21qoOBLXClJWCZ9uQi+iiLwcKVCB+sqQJH3LZKRKv788lY2d/TSnXPxpcIyDGKWQU9e/7cznWP/uoGBQfFIglgkrKcXsmuEgikkZJIYrULCaJT7hB7ftJOcr4KEQ4VUWlzoV5BzfYSAqKk3dOfy/PipDcyvTSFQrDjwRRqSXVgGeFKQ83xiposCHtrUCEr3Usq6MKvmQNp6m5CyP4XEAJraUmzqytCT304qatHel0cIgSulbgdnCrb1zKAh2cu8GQmsIPhCKcWcmoNCM17ILhN+YkJCJonhKiS80v4y3TkX4Y9s2iomv+Y9iR/kGUrol0hlKMAUkrjlki2YpHt8JHle787ytkM6mVvVjVQgFRhCkYp4SKWw6/t4aksDLT0Zcp6PIX2++pDgqLlV2A19pKI+fQWTpvYUf9s2G0PkaUjFaUzF2NqVQaIlpCclyYjFgxtnki54uLKX2VWCA+obmDdjoBDendD2kH2TUDCFhEwQ5QuvZagBFRKUUmztytCVK5AprGVN0yLekkkOCO/Oez7bujOs2tDC690ZOjIFMq4/4vUEirMWt2PX91EV9UkXTNa3pVjVXI+nPOZUdwAKhCCwzaGAZERSFfNIRnxmV8dJRCyad3TSmnF5oLmBNZtnUhWX5F1BVVQgUGRdj80dvfRk3VL3WsswUChcCaYQPLFlDmmvhpjpcpZ9AGcdegSwe6HtIfs2oWDaS1m1ahVr1qwhnU5z6aWX7nbH2pCRKQqi2kSE/36iacDCu3xRNQfNSONLRdSKsK07S1tG+4MSlo8ShVJ49w2nLiuZ7ja399Lc3osnh1WQBrBiUTtHzelBIfCUIB6RHD23B4BnXp9B3JS4PhjGwJFMoch5Bm0ZQd7r4fB5M+kp+EXZhScFx83rwm7oozrqk/W0X2rVxnoQBrJ0nEKhSAgteOviMcAk75usaW7jg6f4xCyzpAGOJ7Q9JAT2McEklaQ3175HY2RzWTwjU3pdHa8fM4FwT1qrF/nzn//MOeecM2T7r3/9a37wgx8Maac+Va3UQZc0ufzyyykUCvi+z/Lly/noRz8KjNxKfax9lUym4PKV1S/yxOaddOdcsgWfvO+zf20qWHhd1r76VyILtmOZEoVBxjURxAFBzo/Qmzepier25JmCx3894dDSm8c0ZEn78eTInyvLkCxt6EMxsGadQrC0Mc0/tleRdk2qPZOqqAdlx/kKXt5ZhScNOnMem9vTeFJiGAIlFWcuaufoOb0oBK4UWIbHkXN6kApWNzcg0EKzqDkJoD4VYWEdSHzApCOTLwnpNc07StUhigwObQ8JKWefEky9uXZ+t/bWsQ/cBd527L8xI9E46jF70lodYOvWrdx3333DCqaNGzeO2k59slupg65dd/vtt5NKpXBdl3e+8538/e9/Z+7cucO2Ur/kkktGbLN+ySWX7NZcp4JMweXLDz7PD57cQEdWV38XwX8RUy+8s6oS7F+ziYW1HWQ9g2pTp6vGrQKg6M1H+cf2JOvb80R7OlBSscrZQjzicfbibpbUZ4aY5QYLH4CqqK8FjgBfitIxdQmXVMTnmuNeJ2r6gCRdsEhaPqah8KVgQ3uSBzY2AFrAZd0eDGFgCQvwWdrQp31aZTqbQrC0oY+HN8/EkwaWoc2DSilOXNDCkkC7yvkRtvXMoNezqU/FSuWPhhM+ReEVdsENGcw+JZgqDd/3+cxnPsOWLVvwPI8bbriBhQsX8rGPfQzDMPB9v9Ry/fnnn+e73/0u11133YAxNmzYwAUXXAAMbKc+Va3UQSdSFusHDm6nPlwr9fL7H2lfJeFLyVcffJ7bHllH2i1QFfWxDK3R6IBqyPuKzR19bOvu5YR52rfTmbWCQqgeAoEpfP62NcmfNtTqc/IuKxa1s7Shj/k1eeKWT8Yz6cxaA8xyDzY3DJiPQHHigi4aUy4RU+JLQcbTYXnVUYkvoeAL8r7FzISL6wt25KLkPIOXW6tKQumsRW0sbeijKuaTzps4bSmeeb2GqqiPp4YKw1TUpyrq053Tgsn14czFbRw5pwcw8KRBxPA5sK6drP8aMcukPhWjPhUrFYwtZ2ZS75sqFn35d7T0ufi3vmfKrhmye4SCaRq59957aWxs5Oabb6ajo4P3ve99XHLJJZx00kl86EMfKpn/rrzySu68884hQgm0xjRcO/XRWqnD+JoDjreVOmghc8kll/Daa6+xcuXKUjv14VqpF8caaV8lUAxEcH2fi370EM3t6ZIQGUmjESjOXtzK4plZjCBJ1fUtunIpsq6LLyVPbKktHb9iUXsgfLR/yBAEZjfozEaGaClFVixq58g5veR8g4ipMAyojnrBNSHjWaVrdGSjFFz4fdMsWnpj5Dz9lT9rURtHzw38U1Jf/6i5PQihSBdM4hE55Jn0FUz6CiYKcH2JKSTLGjMIYZT8UwqwTJP6aCeZQp5kVAc6FH1MRaRSnL5odmjGCxmWUDBNI8899xxr167l2WefBbSv5k1vehM33HADvb29nHPOORx99NH89a9/Hfb8lpYWMpnMsO3UR2ulDpPbTv0DH/gATU1NzJ49e8RW6qO1WZ9OfCn51iMv85O/NrOtJ0Nv3sM0JBfarRzSmEZiDAg0MITir1trSRdMlh/YwSGNaZQCJXSQgRVx8QuKvGeRcy3SBb0Ql/uIIqbCMnRSq0KQtHy60MKlqKV05Ywh53VmtXaciviYBliGojtr0Zktfq0VdQmPVI3P5Ue00J23eLUzwYPNM1na0AdozccPghhAsKQhQ1NbkiMDH1MRgWJ9W6okIJWCOTWCqqiPVAYIgjEgapoYIsf2ni4OatDRd6D9aR2ZPDOT/VF5ISHDEQqmaSQSiXDttddy4YUXDth+zz338Je//IXbbruNt7/97cydO3fY85uamjj66KO58847J6Wd+q62UgfdTv24447jscceY968eSO2Uh+tzfp08o2HXuSrD71Ib97XCaqL2lnamGbxzCxKQc4z6Ai0mdqExzmL2zlmbg9p16QxWaC3YJHxDOriWoMRAuriBZTyebhFm9Dqk3lqoj5VUZfquCQZ8YmYMkh2FTpn1VB4UtDnGpiGJG55xC1VCo7wlcA0VCkQoegNMsoscHUJj6qoh1SCVFTSkMqxpD7DCQu6sExBdy6KVKokUBRayP116wyk0tpaMZ+pqB0KAQnLZMGMBHNnxMl6W4kY/RF9CCj4PqaIknN1vb6YZfLh0w/lg6csrYg8JvPffl76OzTrVSahYJpGjjzySFavXs2FF15Ie3s7t99+O7Zts98dSLYkAAAgAElEQVR++7FixQpqa2u5//77mT9/Pp431EbvOA5Lly4FJqed+nhaqYNup25ZFjU1NeRyOZ566imuvfZaZs6cOWIr9dHarE81reks977wGrc/08zjr/ZHbRZNbaYBUVNhGIpkVFId83GlgSl0YAMCqqKSuoRHcb3V+ocWTEJATdznzQvbePPCTmring6YsLQwcqVASi1oTKGQ6ICGukQBzxd87ORXiZqSgi/Y1hOhIVXANCBmSgxDF1z1fIGnggTahKArqwMeRBCeURX1AwGm/xYIVKxAR6B1FekrmKQLFg82N/Dw5pkDIgQNYFYqxjUnHYxSsKqphXU7Exw+u6dfu1KQdz3+vj3JbX95kHk1Sa44cRE3nXYIMcsMAx1CxkUomKaR8847j6eeeop3vvOd+L7PddddR0NDA5/73OdIJpOYpsmnP/1p6urqePnll7n55pv51Kc+VTrfcRxOPPHE0uszzjiDL3/5yyU/055S3krd933e/va3l1qpg26n/qUvfYnOzk4++clP4vs+SinOPPPMUjv1Yit1y7JYtmxZqZV6eZv1wfumimyhwFG3/IGNHX3ELY9ZKZe4FSHnWQNMZjUxF0P0G7YsQxE1g4VeweyqAlnXxFfaDFfSX/rzWokImFvjI5WPL7VfxxLa5AcKNzCRGYEWVBX1ECiiliIVkSgElik5sF4SN7X/xzS00DOFwhKKtGuQdU3ipiRiSoSAdMEkEZEDcqJMA/oKgkTER2Qj9Cs7A811njRKJkTQ2ljWk3zvcQdPQsb1cP16PMnw2hUeG9p6+MrqFwH4t+XT88Mj5I3HPtVafULymDJZEsn+dgPjyWOaTMLW6mMzXIvu9nQ3J3/7V7zebfAvx7SwqD5LzPTJ+ybN7Ql++fwcrj3+dXwlmFeTJ2ZKLENrR0UBpYMA9AIu0MEOlpCAImJqoQHB8cF5pdqog8bx/P59RYEnDLAMvc1XWouKGIqC1LXxTKN/TKUg7wl6Cxa9eZO7XpjDhXYrVVHJvOr8AMEkJWzrjTIz4bGjL0rMVAMEyuDwdMuQzIi7xAzI+gmyBShILRylCsLlDUl1XIfQd+cieNLAFDpiUynF4sZqnvu3i3bJhDfRrdUvvmcDLX3ugPNCU17YWn3aMYQxZs7RWFiyj1SisgRByPjIez4vvt7Kg+t+TtLs5KY3SUwBhtFverNMn8Nmp3n3ES2kCyZVMR9TDP/jrZjDBP0+ns5chKqIS9RSpTEVYJSdMzgtSV93oJAp/i3QWpUlQAil/VZSby8Khf6BBMmIpKU3ypbuBOtbqzhmbg++Ev0an1BkPROFwes9cX64dj5xSw2b0CtQnLWojVMWdlIb1xpiT87isVfreGhTA17p+orTD+zAHhSx+NCm+tLNbu/Jsq07w4H11eN7s6aIcn8ThIKqUggLVYXs9fhS8eHfPUHqE3fypxf+k8ZkO9VxSczSAsEQ/UEDAq2J2LMybOyII2V/AdSi0JCKIIKueF5gWhM+L2xPsWpTPTnXwPVEyc80FqLs32LQxGAhZqC1neKBsiyiuygYTSF5tTOBJw1WNdfzbEsNPTmTiCGJGBJLKBKWz8xEAactSc6z6Ao0nMGsWNTO8oM6qE3oRF4hoDbhcfpB7Sw/qK103RWL2jlqbg/xiBwQsXjmQdo6oe9F31AxDL8nV2Bbd4a8118L0JcemXwPvhzqTw3Zt5gWjcm27a8Dbw6u/xXgGeDngAm0AO9xHCdv2/blwE3ob/4PHMf58XTMN6QyUEqVEnfHIu/5NO3s5oGXt/HxP+kW3+cdvIO5Ne6AyLUiRQFSNKdFTEVTW5KD6nJYhsQM1m2pBgoaw4AYoJB4EhbXZ0kXTLrzJvVJd4DJbpcp2f6ClwKynsAUCk8WKz4orMCkFzUkri8o+LrwqkLwYHMDplCcflC7juoLblShfWX7z8iysy9Sym8qYhmSZQ1pEpak/A50EVjF0sY0D2/WEZUjlUayG/p4aPNMTEzmVse569nNPLxhBxvaesh5PgnL5LC5tSxfPIszF3fSOqg9iFJhoESlMNVr9pQLJtu2lwOHOY7zJtu264HngNXAfzqOc7dt2zcDV9i2fQfwWeAEoAA8Y9v27xzH6RjvtQzDoFAo7LJPIqQy8X1/zPfSl5IvP/gPvrr6ZfJ+v0qRsvJcvKx9WKFUTklLUXDE3DSpqE9P3qLe9BCBZlUysQXakwRMAZ7Upr/ZVQVMQ5sJlRoo8IpCrah9ASPOSVDUxfR5UkFPzmRHOsbsqjwzE96Qcz1lcMScNL4yeLC5AcuQHFyfoT0TC7RBhS8Vc2sKvGVpK+fbbRQ8g+aOBN9/ej9kYESpivrUxHUZo8FCxxSKmpjOryoeO1qlCEEUDMFta16mz/WDJoOCnOmzfkcPC6o3UWX0sl9dVak9yGsd6xBeDXDc6G9YyKQzlWt2kenQmB4Fng7+7gJSwOnAtcG2e4GPAg7wjOM43QC2bf8FODnYPy4syyKbzZLJZDBNc9y/tkfDdd2SE74SqLT5wMTPSSmF7/v4vo9ljfyR3dTWzUnfvp/WPn1tHWlX4NBZaS5Y0qoDCcaJlLD/DN1GPBlEtZWb21Twh5RaaPhSEDUlMdMvmeKCcnKlY8qFUtGCNVYsgAIKvo6AyLkGed8kainas1FmJDxiwZgSnQNlCkVtwitVjKgKhIOntJbkSR3MkQoqOyhfYJmKpY19fOCELfzn0wsBHc3XkzNpSAoGd6bwlaAnb5aShYerFCGAPtckYiYAk+3dWbKepBB03/WkwpOKnnyOOVVddOUk85UqVYcQQtAnd+JLb0obDQ72OUHod2IK1+wiUy6YHMfxgWJm55XAH4FzHMfJB9t2AnOBOUBr2anF7aMSRJeE7EO81pvhsntfoYA2QdXFXd59ZAsH1uVIRPxSgMN4KGpBvXnB/Jo8MUuStGRJKpULKCmhtS/CzISHQhC3BoZlw0CBVNoGgUltbDOfIXTEG0A87lOX0BKt4A9MrJVBCDoCkhGfqphXCkQoFxwGkoRZVqK1VEpIsGhmlrjlkfMsPGmwrq2KuTV5UlG/dNcCyLiC9a1VJb/U+rZUqa5fsVCsIQTbemqYn4ywsStH3lOUBdIDWjghs5jkyBYEHd09xMz+N0oqyTPPPkVEJJgIvnZOE+/97YG7fN7atWsn5PpvVCZ7zR6OaYvKs237YvRNng1sKNs10nd1XOpOebj4ZLArIaxTQaXNB6ZuTpt27uCSH/0f7QWoThocN68XuyHN4voMMTPQIIyRTWXDIYQOAS/4UB31MQLpUYq+K2ttbghdzVtH1Kn+gAXG7qU0XuW9GIxRbgaEoJV62ZwsQ5VyoJSpKzj0FXRh1ab2JMfM69Hh5kFtPdDCrHyeUVMyK+XyWrdeFnQukuKUhZ3MiGuB2JWzePzVOlY115fOW908kyX1fSyamdWJwNJge7qKjvwSEkkwez2U5yKEQJSlpyigO2/Smde5V9sykrqExYLapNaYerMcf8w/jUtjKgt5nnAq7fs10Yz32U3Wmj0c0xX8cA7w/wHnOo7Tbdt22rbthOM4WWA+sC34b07ZafOBp6Z+tiGVRktXD9966DscUNfLDUHd1/L8oKIgMoZtGDE2cQtmpRRuEI032I6nZBBirtDJRvQHR9B/2KjsilV5uBDzIqpsrFJUuwLTkCw/sBMFLKnPMCPmB9UjFDJoQugOqtNa8A129kXKxhY80NzIQ5vrmTEoR6mcMxd1kIr6bE/HMA1FIhKhJg5L67fwQut+zKqK85rr40lZ8pUVb6cgDTa0pThmXi9SKdoy+kf4gtokKWPWlJrxQkZmqtfs6Qh+mAHcAqwoc4qtAt4O/CL4937gr8CPbNuuBTy0rfKmqZ5vSOWwcWc77/75fVxyyMvYjQPX6pHyg3YXwwArWLjL68AVNZfi4moJWUqK3XMP5kCUGluAlec7Fc/JeAYd2SinLOykr2AgMWjPRhAoZiYKeEprXIZQ+ErX5BNAc0dySHQe6ATi9szwVohihQwhDAwhkEqRcSUCqI2385ZDT8CTBrc89CJZF1wlEWVapyXg5dYFLJjRwZyqLmKmS2uf5PgDluIVKiNfcDi/Uzl7uw9qOtbs6fg58g6gAfiVbdvFbe9D39C/Aq8CtzuO49q2/Ungz+jP8ReKTrWQfYu23jRX/uIHLF/cztXH+7tkmtsTSkKhmLMEA2x1OmRbm9rKhddETG+89Vik1EqbUpB3Bb2uSWc2gkAwI+6R9aKlfKfahEcyKvGkgSchYUksQwvZdTtTfP/p/UrjWuPspKuj9ySeFIFGJIgagqhlYAofUxS4afmxPPlKK395pZWCJ7EMQVXMoqMvz5yaBPvXVbG+vZqmjv2ImS69BYP3n3Q8La3r9uAJDs+P3/YSV/4ubOe+i0z5mj0dwQ8/AH4wzK6zhjn218CvJ31SIRWHLz26Mt188O6nkf4LnL2kg3hk1/xFe0p5kusQQRHsMA2dR1SURlIFgQ17SFHQjTmUoNQYcFtvDF9pIVKsPC6lwDK0+U7X8ROYQkfMCbRvquAJNnYkUbq065h9p8rR0XsGiYhE6DRaPKkQvsQTUdY4nVzxTx6/+ZfTue3hl1i1YTvpgktdIsarHWkaq/sDG6QyyHoxahMW9akYLXv+GEMmgOlYs0MDbkhFkXVdVr38IH979Xmqor2ceaBfKtcz1QwxFTLQl2UaqrSt6N/Z01Iq5XlPgtHNecWoPAOIGIo51S4Z16AzG0FK7UOaW1PQFcuVImJqs51p6OAIhA4QiZiKY+b1IIPSRTpQor/v1DFze0hYPn/c0DhEe/KkUYrKE0KUwr0Lns+6thhPvtLOyp89wrlL5/GR5Ydyw2mHlFpffO/x9fz+pa0D0jjCBoIhEAqmkArBl5L/d99aXn79UY6d10MyWqAuoYYEFUw3Al27LiigAGjh4QeCqagt7a7Pqei/yrkQtRg190qgNUgJdGdN4paiOurh+YId6Si1viBl6iaC0SBk3jK0XdK0+ns5FU2Sb17YTlVMETEUvhJkXH3xZEQyvybPAbVZ1rVVDdGeVjXXYwg4ZFaWZMQjXTBZ11rNX7c0ErEMCr7k9y9tBeDDpx9aan2xtzQQ3Nd9UJNBKJhCpp2tHd2c+M1fcdmRr3Px0hzGLoZ4TzaDhcxwU1PoxV0JRg1RL+ZJFUO2hztMAPHI+ARb8ZjOrIXCwDQUmbyBYSg6slHikTwxoaPhitpXUfgVzzfQmlVE6EzgouZUF9c161ypTXypmF/KV3qwuaHs3gUPbGxgzWZJKirpzRv4yqAqCnXxaEmLWtO8gw+esrSkDZmGMS0NBEM/U+UTCqaQaaO1N82Hf/MTljW28YUVhYrTjsYbgCCE7rcUMcaOpNP9k8qqiJcdq9Ch6KVjBlcPH2lMoC7p054x8aSgKi4RKAq+gVBB5YjALhi3lNb4hA6c8IMgjoQlyXkCpK70oLUxPQEhBX7wH8Bhs3p57NXaARF8Csj7Bvls/5toGYIFtf317joyedr78kOaBYYNBEMGEwqmkCmnN5fnhFvv5eCGzSw/qINUdHpNdipQiQYLgd01xY3ruODYwdcub+01nrGKlc7jpl8q3NqbM1FCR8yZRrnTSiBRSF8HbejqEQYChSEUOc9CKt2kECH6ry8UGc/UUX2Wj2UoPnDCFl7cUT1iUARAT95FKoUZDDQzGaM+NXnJ7yF7D6FgCpkyMgWXd9/xEE+8+io3vekVZlXJXUo0nQyKJi2CItr9rSymhvL73xOflGmooBwQrGurRqBYflA7kaAiRNGE6EuBH9Tv86XANCRS6koXHZkIKtATk5aPCvpUpfMWoLTAQuApg4jBsGa9cjwJr3SmWVRfs08HNYzlgyoS+qL6CQVTyKSzraubK//nD8yteoUT5vVxwRJZUT4kT0IkWC8rzJo4JsUACFNAX94sBSecs7gVIYT2ZwUH6qKyOrzd9Ysam47Sy3sGdYkCHdkIndkIXVjUJwv4CrqyFvOq8xCEg2dcIxBfolQsdqRcp+5MgcQckzMPnvOGC2oImT5CwRQyaXT29XHNnd/njIPb+OfDpns2w1MeXVdBsnLcFH1VnbkIP3luHpYB5x7cylmLO4iaElcKXCmwTFnygRUkRA2IWQqpIOPqVhq1iQLVUY+Ma9FXMFndrDvQHjqrF8tQeMoohaMXKba26MoNFUyWAQc2VPPpsw7n6AX1mOOtpBuyzxMKppAJJ+v6rPzhfdiznuZcOz/uyt4TiQp8L2Nduxi48EalGPIdMRXXHLuVOdUuqZhHJLhvZVKqjZf3tG+pWIhWBv/qVhkundkoOU/wy+fnDqiJ9+irtXzghC1asA26fl+hv/3FYKSEja29fOSev9FYFS+FgocCKmQsQsEUMmF0ZnJc9cs/cNJ+f+M8e9cKlU40CujKGdQmps9sOFYu00TV1zMEzIh7pKIe0bIqFEUpUkwE9qXAQLefGKAlCp2r1JWFVETq5Noy01zOs3hxR3XgU+qfsUCxvi01rBmvuMWXiohpkM57A3KZpptKDBkP86H6CX+6hOwxbeksR978v9z+ly9wof03ZianVygBoMBXBllXlDQDOfjn/sRdSneyVcXGflDwGFe8+WiH+Kq/79JoGEFUX8ToL6NUNFEWGxYaAiKmRAWSqFjyqFjJwhQ6eKKoAVmGpDbuYgW9oFY11/NcSw0518AUumnhcy01A9pfDHdfBV/ywrZONrf3opRiTfMO8sUuiSEhIxBqTCG7TW8uz5u++Xte6erllnOaiEfGPmcqSUR0bTgjqBc0acJS6eTa/hbrOqhACB1oMNJ1S4rNCLlPrge9BZOZCb+UWzTC5Uek/BwjsPsZVn9572JPJqkEUoLTlmL5gR3D1sp7sLmh1BV3rOKupZJKQNr16M17tKbzzKqOsbM3x351lVE5PKQyCTWmkF0mU3B56w//zKzP/JJzDn6Z/zi/8oSSYUDCUsSKvSsmEYV29EeDb1NfwaK1L6qFlSqrPD4CwwklT+r/tvXEcH0xolAq72001hx9KUrllEBH5umxFT05k2dbagDF0XN7iEcknhIkIpLj5ndz9uK2YF4GXcP0ZBp2bpQnDQsKUtKdc7nruc3jmHHIvkyoMYWMG19Krv/NE/zv39ex8tAdfP3cNKlo5UazTVXSbrkPS0DQihx8JbCUGiAMxkOx4oNpwIF1WXylq4MPjhmQZea4oq9oyFj0F4P1pC6sl3UFqZhueeFLRXfO5InXZrBmcx3XHr+1lDBbl3BJRiSmUDQubgfggY0Nu9x+UQJGkElcE4/y+Oad3OD5+2RO054wkg9qb/Q9hYIpZExa01l+9KTDlx5Yy1fO3sg3zp0kZ80bnOJTiRi6zpwSu5msGwgyw4CUqUO6i8m/g2v2lZcxGmlSquwE3weJ0HX9ELSkY3jS4Ig5aSKmoirq4ylBXcKlOuqXKphHTFmqQD5SQu1oKCBuGexflxqxNFFISJFQMIWMyI6eNId/9XdkfJeL7Fb+48LOiqtnV0kItI/JNPvzi2BgG4vxjiMJWmmUC7dBg0i0oClWIC8fX6ogoZZ+jckSEsMq+t5036Ri/TuF4MDaLGnXIGEpkhE5wHdVrJU3VkLtcBRLEjVWxTGFYEZYmihkDELBFDKEgudx8rfvZUtnK/9y3DYOmpkjGlpddonyxN2SkBqnSU+h214UC7mWcpKC/5WqgwcmP2EwpL178dpFoVbqvhuYBIvzm1uTZ1tPDBAko5KXd6Y4dHYaU6gywaTIeBYKUUqoTRd0Lb6CHyHvC/xRQh4Virhlsn9dqqJKE1ViyPjusDeGmYeCKaRET67ATx53+HPTKt62tI3ZKX9akmP3NkoFW8d5vJL9rTOK5ww2CRqCUpVwGGrKGyIABZj0+6mK2lzSktQFybV9BZM/bWgg7xs0Lm4nYkp8Kch4Fp1ZvVT0FQxOXNDFkvoM1XFJ1rV4aWeCBzeOXMzVENCYijEjHnlD9lsKmXpCwRRCOpfm/Xfcxc5MmoZkgYuXdRALPxkTzri0JQU+us14X8GgNu6N6Kcq18rGe+0BFc2Df1OWTzeS9W3VuNLkzxsbAYJOtqIkcAQKqQyOnNOLQpv2oqbPUXN6UGr4Yq4RQ7KozuKnl5/MkfMbK0JTCql8wuVnH6Yrk+H/nv0Rvr+NC8IfsZWBKPZ2ksStyalaoegXSkX738utVQOSZR/Y2IBU2qeUivr0FUya2pMsqc+gEAgEShXNfUN9TwLFikXtHDY7w6yUoKPnT2w0F7Fs/ikYIlTDQ0YnFEz7IAXPY8X3/swxs5/i8DluaK6rIIYkxE4SRX+V58OmziR/2tA4wBSnEAMSanOeYFbK5dh5PbjSQA0yTA4u5rpiUTtHz+1BCF349YWWnWzp7MQHjlhw6uTd2C6yt/iZRmM4H1Sl+51CwbSP8eLrrZz/g99y7pKdHDHXnf7SQSETSjHQYiyhppQueeQr2NyRwJMGliGHVHXwpeD4+d1BJQiPhqRLzh9YYRygzzXpC4q5WoZkaUMfCkHMMohbJr5UtGUKPLXpHxw67yRMI1x6QkYm/HTsI7z4eitf+OOPOWtxF59fUblJsSG7T6kmoNSReoN/dBSFUd7VQRC+NOjJm9y/oZ6zFrUNW4aoqPmooEFgzjeojmq/l24sqM12TmsKqQxMIZiVgpq4xBAm8TKfkgB6cml6cr3UJeum8tGEvMEIBdNezpaOHo655S6+eNYmzl863bPZd5moSuKjIYQOCzcM8BQ6NFyAK0VQ6FXRmbPoykYwDV1N4tmWGZx2YFeZ8BHEI5Kj5/ZgCFXyKfXfhW7DXhd3SVge3bkIf3mtjlXN9UETYEU6b9KbN0hFFTnXJx7pF059rklf3qQuzK0NGYVQMO2ltKUznP/93/P2w5/na+fLUEOaJgo+5F1BVXzyq2UUYxlAC6isL+jJRShIQcY1cT39KUhFJem8xfq21JAyREUUgkNmpYmbEk9ps15dwqMq6uMrA+krWvtiuH5QWZ1ioi4UpGB9a4qj5uqoPk8qqmK6PXt3voHG6sop4Lq3+5cq3Zc0EqFg2svY1NbD+f91HxcsbeJfT8iGgQ2TyFhakCuhO2eRMCe+zYMuyqqTcMtNdsWaeQJI5y2e3lrNsll9xE0fpUzWtyZ5aksdvQULTxrUxt1SGaLBxC1JzjWwTK0JJS2/eAV8ZVDwdYv18og8ARQ8yepN9SDAbuijKuLTkzPpyjdw8OzjAdjWnaE+FQvDx0OGJRRMewnrt7fzpm/ew7l2K9ee2MmM+HTPaB9gFMkkFXi+QTLiYxkTry0Vi7yO1gqjKuZy1uJ2oqZu/SGVYGFtjoPqcnzv6f0BSAf9l+KRoVXY03mLpvYkR87pxTR0vyYdKg4Z1yhpZ+URecXaekIJHtzYwKOv1JOM+ESMBDeedihSCf759kdo78tTn4qFXW1DhiUUTG9wNnVmOO1jt3PFcVu4+dw0UXNyw4z3RcpLAZUzUkSjCvozmYYkOs73otju3JX6XAFEzP4mfyOdM9IcDCBhBj2iAsFoCkUMxcH1Gc49uJUnt9SVAh2KPiaBKvmf1rdVs6q5HqUESxvSyCqdbJtxB0blDddevSizI2YUheCA+hqyrscjzTsxhCBmmRXX1Takcqh4wWTb9jeBf0J/1m90HOeZaZ5SRfCPrW2c8u37SMRcrj3xNZY1FkKz3SQxUkuJkShqEqUmgWP0TFJAztMX8SVs641Rm/BJRTxyHtQmFGbZAKXisCOMVRRYUlA6r9TZ1gRDSM5e3M6x83vozlk4rVX8o6WKkxd2MSOuzY7dOas0/gPNDTy0eSbnH9zKssY+yj2WI7VX78/dVcxMxpiZjPK3LR0YgySpIQRrmnfwwVOWhma9PWQy/UlTvQ5XtGCybfs04GDHcd5k2/Yy4CfAm6Z5WtNKS1c3R936K958QCefWt5DTdQjUcE9kaab0bSKcZ3PLvZSol/bKV5/rAEEEDXBDySOaUBnNsL2nij3NjVy2REtzKlyh216WBRSvhzYf0pJMNTQHk5FE2BVVBIxCzSmXOZV53m9O05fwSLrWUEZIjhqbg8KXWrIkwb3OrPIeu0DqkEUw8pHeg51ySjzZiQ5br96VjW1DCt8wjYYlc10rMMVLZiAM4H/A3AcZ51t23W2bdc4jtMzzfOacjr6Mnz+D79gdtUWvrTCLbU6CBkZOVqfol2gqAGNZ6jB2tW4a9kJsETRDCgwkGzuqqKlN0ZPLkJVVBI15YDxlIKsJ4hbZQHdMggVD8YcSQcRAiKmIooibklq42k2dyZ0M8HiWINKDQ1XDSJuadNf+XkGYBqCVNTikNm1nHnwHP71pCU893oH6bw3ZC4zwzYYlc6Ur8OVLpjmAGvLXrcG20Z8IOeddx7t7e2TNqFCoUA0Gp208QejlGJ7bxbT8IibEkOMs5d2yMBy3rv7zCZijF24lgJkECGnFMxUgt/8BoRQ2jRYNiEJKCUQYmhd76LGMqpgHHRvUgp8NWATAKmCVZpT8cSIJUkaOqcpqQSuFOQ8A4LgCCGgKhbhtUSUnwE/A3pyLhnXY+BIkIxYnPDtgZUkYNe+a/X19dxyyy2jHvOdT9xDa1tr6XV7Jko1vx7X+G8Ejlz1jd06bxzPbpfX4T2l0gXTYMZcGlzXpVAoTOokJnt80F/Y3oKHxCUZUZhvVIGkRlnbR1pNJzIbdaI1pl1Rn0YbcLjzy/xRIni/TXQ0XTEfyBRBWXAl8BRYhhZKg4crz2kakSHBHHrc/oALHYUXNWVJ6IAiYUkipuy/FaGImvpqOc8sBYtEhcIt+67EDPAMKPiq1MgwagpihhrxOzXe75rruuM67o3K3NRQwT2Y3V2XduPZTfpKVOmCaRtaMheZB7SMdsKqVauIxSbPLLB27VqOPfbYSRs/7/n8Y0srX77/Ts5Y3EZVTL0ho+wU8FpXlGREkYx4dDw6E9EAACAASURBVGUjZFyTvC+YV50jEZFYpsASqtSKQTGxEYV7It9UmZD0pMCTgqglS6u9GCVabjRK5kUx8tx8BXmv31YbMXRfpNd7ouQ87dd5ckstpx/QznlLOkqN/wbPf6ReTiPNy1fQnY+QivjBuYp0waIra/H3lmpt2mtMs3hmFqUg45lBnyZ9gZxr8P1n9iNmRYiZJtcsP4SPnXn4kGvlPb8ULj5awMOufNfy+TwvvvjiqMdc/7WLcVWm9PqNlFzbNYmBDeN4dru8Du8plS6YHgC+APy3bdvHANscx+md5jlNCp2ZHDf95kn+sH4T7zliKxcekn1D+5GkhGREURv3EEIxM+nSaLjokjZ6KTMCoQT9UWMTxXiCDkZDiP6ac5ahyHtCtyc3dl/gKQXdeYhZgV9opFBvARFD4UpBxFBB00CtQVkmHD47jVSCY+f3DiuUBl507MnKYkHXoL8SKOSABoGCUxZ20lewMIz+JoVVUe0vKoaOF/OZenIG/z97bx4n13Xd+X3vve+9Wrt6xU4CBAGiQFISRcnUSkmkFspyxpNxEmccK07seJXlmTiOl4nn45nx5BPHM5OxHDm2Y8v+xPbYHsd2PJ6xR7IsbpIoDSWSWkmCRRAACQJoAL1Ud1XX8pZ7b/6477161d0AmhJBElSfzwfo7lpe3feq6px7zvmd38/6lj949CQffsdRqsH4br/kqW2gw7VlL7kffkUHplar9flms/lYs9n8PK6k/uGXe00vtkVJwgd+8z8yUTrOwak+v/juAVX/0k7rWjEhYLYaI4DEOIeqxKi4pC1jEOgX0ywvDughR7QJCFJtpG8q3gmoB1mB7LIPQ0qLMAI5Npyb9p6AdxxYZkctueRxcpbxKwTSbOYqMU6eohsqdFo6LAoETpZ1jtjT1g3sCgRVT7OCk13vRYp+pAiUxJeS51f7/Mv7H+efffvtL+QqbdsrzF4OP7ylwNRsNj8AzLZarT9sNpt/BLwJ+LlWq/UXV3V1QKvV+kdX+zVeDgsTzedOzPMv7/sz3n1jm5lqjBAWfxNW6GvRsvkdSBkK1jXor+Y5Xo1DBy/CiI3AZVwm++MyJsV4FpmkGQ04zrpGkORy7ZeaZxomgopv87+LJb0scGkDoVb0Y8n5bgAWSv54dyob0M1g5INYUg9cuS9jhNAGnl6sUfJ8lJRY6/phX3huiTDR2zNKV7BXOqfdS+2Ht5ox/RPgO9MApYDbgb8GrnpgerWZNoaf/4+P8qufOcZ33LTAew6tMF1OvuGexSvVbN7Ed3+vR2K9mNiBl4K5+8Wyy0G4xx4HgMVaV2pbHXo5M0PV0yRWIowhkGx68sYAaeDJyn1m3WBue6BYDX0SLQHLsYU6QM4CUTxWJ/QRaTOwPXByF/XA8Yn3IsWxXAFXp//Al4JHnl/kf7/36/zCPa+7JO3QVntO2/atY1sNTP1Wq7XYbDb/M+DftFqttWaz+eIzU77K7anzbV7/r/8939Fc4Cff2uem2cGVewTfoH2zg6Uvpm2KGEujSeYwLxeUi4En2+kXM4AXGqQyZy9wFECxEdRehvLpldYaKEcNFGpBe+AhsATKoKShG/kQK6YrMf66g2gDkZaEWhBpSyVIORisKyP60p1r1TOUvYjVocdDz02PDcrePLfGRFnTHSqOLU4icAO3GSKkH/kME8HXz1f5+PEdhMnGM9FYwkRz//F5Jsv+BtohbQwf/cwxHjxxYZs7b9vGbKuBqdxsNn8G+Hbgp5vN5k3A5NVb1qvLHn32Au/89U/w/psW+eV7lqmXTNr8f3Ffp4i2ziC7mbMtorSuON/yTb5+9hqIKwQd60pUZ1bLXD85HGMuWP84AzloIjuPYgYwTKDkXf68sqCWZacm1SsaxhJfanx19a7NZna5l7EWooQ8c9ldj1IwjHWZCy7jEVimywlK4a5Tek1cAHMZjmMgt7mQIMAwFsx3S0jproP7zIh8TsoK11vKxpfuPTEDWO480GayrBFYerHPMHHzT1KMXnt0Eo5i6XxnuCnt0Ec/c4z/8MSZl5w771pC432r2lYD048APwz8QKvVGjabzfcDr8rez4tpy2t9dv3TP+d9hxf5hbvbzFZTxoYXGYEGqXppWrqJDZQzFG/q1FcGHrXAyRZYCyVlrsitl5gRg3Wuilq4P3PyeaO9cKcoRKhNszfhWAr6kSTwzGX7JdkxYONsTtaHKW3xk5z6aox1WYWvDBMljbGCWAPCEqitvz9XA+YOpP0bN7gaKEsMnF8L0EYyVYmZSBFxy4OARinJy4PSVeXcLBIu4wKXFQprkdL93k88DDK/P2N4uPvgcl7KC7Wi7Flu39NJ1yToRR6BBxVPUw8S3n/TEoemBylb+fhF8JSg7Cnaw4jF3nCMdihMNA+euHBZ7rxXm73S+0ivJLvs17nZbN6Y/joAPlq47eNXeV3XvH3puXO877c/wYfffI7Ds30q/tWbR7LWEX8OYknd1/heAliUdNQ2QjptHVfCsWPZ0+VMpoGtiHIrBhmLE8EDS8m7RIDK/xu3LPPpJ5JEXx6llqWAurAWkR3WZrv7UZC8lInCLyYFEriAJLjQc05fYNk3OaTqbzxSXgJMs4MwkUjhst/AI0ftZc7eoesud2Kj425gLmcEOpByFGAsI3h2WRlmqyFKFrSZIM9ySJ8jJQR2VKrshpKVwXgvpxZoJssxR+d6GzCDFsHNO7p4CqYqST7nlAXkw7N93nd4kb99Zke+RiUgSFPgWFvqgT9GO7TUc/x4l+PO27ZvXbvSPvM+Lg/8uXGT279lLUw0nz5+hl/79F9waKbHP3rHgOmq/qbLdpEbLcH3Lv1GDGIn+jZRSrAp3FcbSICKhGowTgB6pfXYNKOw1uIrmzpkke/CYy3ohIpf+vRB7rlpiXsOL6eUOSPnnTncS61ZShjEntv9XyaLzFbei1yGY3DzPTrNJrZyPsXXxUKkBYNEUfN1HgCmKjFVX1NSdiwI5edQ6IklJluTz69+/jree7jNm/atjmaz0lJhwIhctVhKzc/NjoJ5FqzzcqIlh4snRuSoPICVgU/JS6gFevT4QjktK1dmwSjUDhHoKWiU3DkWB2R7qWzFZqKB05WYup+gpKNFMikFUWZKWm7Zscb9J2eJU5ZxbWEtTFBS4EvJuw/vGgtCszXHj3c57ryrOsG5ba9ou2xgarVaBy91X7PZfPuLv5xr07Qx/NKnHuZPv/wVDs30uG1PD2MEEyX9Dc/q2DRD6ISSMysVnlsp8/6blggK71gR8rurHlJWBk+5BnfWnUjMyLEWndaVLLGQaEcxUws02riswqSUNdq4WZb3HV7mlp19Yi0xxhJkg6NZ6W1dhmULa8ia8VXf5L2osVIhECfQjSQlz3J+reTkvf0kHz7uJxJPWsrK5plT9txNA6KBoRbMr5UwRlBuuAbLRClhInBQtiwjirTAWIGSDjCQBT8pQAmBEVD2NLft6fEnX9/DTDlmb2NILdBpxjDKrrLrL8VoYRnbQjEojWVPAkQaBPuJyjOZ6UpM1TcESrvh29HlHp+1ykq51gVHVSjzKQkTQYLE0B74PLU4werQ3yAaOF2JmQg02gDGbQyyebTYOF48bQRlz1BLxQKL7582lkZJ4q3LjEqe4q5Du/IeU2bGWu46tGsbnfctbludY2oA/y0wl95UAn4AR03xLW2L3Q6///lfYXdpyE++jbwJHBsclPcFmknZoaMEOqHHRx8+wMow4D03LhFpiRDWDV+SZUqCtVA552Elypp8l48dSW8Xd+tFRNxmZi2c65RSWhxB2Q9R0jkrY0Ue4Kq+5l03tPGV2zWLFN5cdL6b1dYszuFqC7uqEeW0bLZJGyolAzV0hj5TlYSKbyAtRwLMd1yw8isxklEQEJe49lJCCcvuesgwkfRjEEiqvs2X6rIldxJlzxBp6YKKHNVAZTqX1U88jsz2ue/kLK2lGjfODFCSPHAWMy2Z/p69TlaalIXHZmXWbDOhLRy7WHXnzShQWEye/a1/L8d+T4NbNh+XvXdKGpSEQGn3ucIFmHHRQKj6Boulnzh9ppKfAXeyR1gGicda5DGI1Bg4RQBlX3Lr7ik+c+ICP7EO/PAP33kzAA+euMByP2SmOkLlXeu23U/65myr4If/F3gOeD/w58A9wIeu1qKuBesOQ/7u79zPu2/4PHsbjgSxuKMufQMbPmvdUKRMPVigQKelkSNzPYaJREmNh0gDAVQ8izHaPUc4h6pSp1nMIDJvkWUqm9kIQi0IMzJOnIx21bNjpaTpSoTElfksjkvOl1m2Vjin7D+RlQfdGoO0XCbkxr5U0ZRw/ybLSb42i0RbgyctM9WEpb6PxdIIEjqRx1QpueymQAioeIaKZ+hFknNdn/2TQwSuXNbPrmEW4AUk1iLN6PngSovtFFRSDzRfODPJ3QeX84xj/SkZXAaYUzJlYJJCepcxNWQBZGXo0VqcwAJHd6xR8132MtQeNV9jNhlehjQTSxch15USpXBribS7+INE8fo9HYSwfPHMJFJYjsz2aZQTJI4vz5X8oOxrqhlYxcJa7LEycPx9kZH5hijrQcXa8ly7x44o4dxqn4OzE6P3Vkr+p7tu5cfvPLo9x7RtY7bVPX251Wr9GPBcq9X6GeBu4L++est65dpqGPNjf/Y5Zv/xn/Kls+fYXXdBaasQ48s254Vz1Ma64FT2DW++boUP3LTA4Zk+9SDBVzbviVjrHEw1cCU0h7QbvUKWuNgR9+iY+mlWOsqcSOYQO6EaW+fKwOP4UpVBrFDCMkwEiRYsD/yxYOUCmiAx6XHTf4kRJNo5S2NcyjDK2tIMrHD9sr5LBm+2dsQw4Bd6LrGGQBo8aZjvlDm2UOd8JxjTBrqUxcYdvxYYbpgO036XZL7rc65TYi3y0Nq9ti8NJcmIJ87CIHaEpwJBP5Io6Ri4L6wFxFq4LIvxkqJMy3u+chmVLFz/sc8Bo/5Qo6S580CbB09N88df28NCL+Bct8Ryf/zaF/tfoYb2QOZ9MArryI7rSfCESXuRgqlKwvsPL/FjdzzPkdk+Ty9V+a1H9nFiqUw3VOk5CM51yiwNPHqhu1bznRJfnp90EuyM3rNsZdpYznb6PHFhlX/wF1/kIw8+gTbj/c6MO++lCErbUPFrw7aaMZWazWYNkM1mc7bVai01m81DV3NhryQLE81zy10+9Kef4yvzF1iLFBrJwanhWBlmvRXh1NkuUqe8cdn92XNHgU3k//djxdv2r+KrhJKy+Q672KvxROFYYuOsT+acsh2IYXR/dp8SoxtjA2dXHXqqFpgxlVIlLfVAo6ThR954Fougn6iUzNMtREknzzAwAoXTCtJGYIXAE4aLvYB6KWHa067X4RlX0iqUpIoErEK4Xld2zbISksUFhu5Q8Sdf30MnUqwOfSbLMf/LO09RsfqSmwWLK21l11OmPZOg4jKf1dBjeeCzimJnPWSiZPMnijSDq/qwdyJECsswUfzIG59nmCgS7UqtWU+qGHSK73OWzWaBbr0CbfaeetKyoxbx7Tct8onjO1gdenlGZsUIuWcK2VysJWXPbRAy0tn1H1CBez0rHNJuIkgRNsJtiG7b3eHwTJ/ZWsJ0JXaZZKJYGXisDHw+fX6CL5yZYi1SGHOF/a0VSCHoRS/NnNLLYduluxfXthqY/gA3x/Q7wLFms7kAPHPVVvUKMW0M/8cDT/C7Dz/NoenTvHZnj7dep92X0YKv9KjJvIllASQxo7mZ+07M8Nbr28xWdb57zZriNk1bjBX0Y8nKwOeG6T7aZFAGm/ce7LqSnJSjUly2pDCB2Mh8wHK5Lyl7hqpv8VVaNkp7PYkRYAXdSFHxDV8tOJ4kdzzuBXuRypvkWYmn6jl0W6Qla6FPN/JS5+dQfHsmIjSKWmCo+np0fUg52AQInDOPTKEUasnhz9m1ttamJSRJo5TwnUcvUPMta5HimeUKUthLgh+yYxYZN4oBzPdgRiUEynDfiRneW4/RZpNMVLiAJoXrtV03pdNBUzfQmp3b5daQ5Q1ZibMs2DSYCuDGKSfXkPWASDs8SQoTz6H01l1HJbN+oE0zcJuX9rLgnxg361RN4d+mgPybqmj2NiLOdcp4yj2mEcQk2vLZ52bTDOnSmWl2tZQUeFLiSUGsLSVPbjpsu23bVrQtBaZWq/V/Z783m837gJ2tVuvLV21VrwDrRzH/+e8+wP3PXOB9hxZ5fdoQTqxgTyNkIkjoRh6RFpS8jQU6V1JJv+gWPGVZCz0irfjss7O868Zlqr7BE5bEOpnrztCnE3o5WaavnJNLGMFzhbRjjfEiki37mWVo3cijPfAd3NvAExdr3LJzDW0NFS8h8BxprBGgY0E/VvmMzJG5Ho+eawCOXeC9h5Y4OtejHowCs8BgkbQHPit4KGn5yvwEXgofNkgSI/CkxpOGTqSoehqyHlGe3bn/E+OurwCsMpkeHlpLJK6UZy30YnemB6ZDBI6zbZAoEJbb93bHWMw3s/Xov8yyUlisBb6CRtkFqNgIfBgDnQBoDdJLEW9AmGYoCFcWDJTJyV/XB6kictH1sgRF1vHiGVigXjbUA53TBr1mVxdPWKJE0k8k3dBDGxdQ6r7GGBeQ+lbgSUspPY41mfKt24y4LM24xybjfHxKkINeHEpDpPpgm1/fDPijBNQChbbgSZluUAR+ClHN5pS2pS+27VK2VVTeP9/ktu9qtVr/5MVf0strGX/Xxx4+TmuhiyfN2NChxOYDhlVP89xKiQNToZunycph2sk2VwPn2DSKTqjoRh637+nwlfkGD5yc5eiONRolTSdUGCOoBXrdLtSxNmcuLTYChMVPd+SJFvjeuNBeJkRnretPlD2DEs7ZvnFvB09BLS29aeNKRQCDQlCarsTUfM2H7jjD6tBLezE6D8xl3yCwTp9HuPt6kcRYweGZAfUgRc+RECjHpCAl1H2Lnzp61wdKIdjWaQ+dapfRRlAvaWp+wnQ1QWuZlsVsnjFNVwxh4lxoYtzGoBLETJVjbAoMYewqFsqb9vL9QIHbEAgsb963AiLVREqdcTFwlINx0EbZt3nJtpQFNLW5wiyQ99Yc/Lww+JwuNushhomgO3RZqkXwqRNzfPa5KT50x/M5+CSz9kByvhPw3GqZo3M9DIKZSoxfSZDCIT5Xhy4rrijrwCcGBkbQToduc8ZwK2mUNLUgSXuB7lq8YW8HYwX3npgbC6AVT+IrxSBO6Mc6LWUbPCnYO1nNYeHZnNK2bdulbKulvCJhawC8E/jSi7+cl98++plj/MXXT9NacDpY9RRxlVjSGRpN2TfOaaRf4GdXKihhqPqGjz2yj27sCC5/+NvOUA9MngGByw6acz1+85HreeDUTJ6BaCPyrMQ5esXXztcp79dMV3T+/ERLlBjJWutUsycrG2qb9p0EeMKircBaS6QF05UYKUdIv+w5QkA10LQHfkp3o1MiUEHF1+ydCNNhzJHgm8XNM33ssX3UAs2d+9upI5QkVtKNJDOVCITg/FqQH1cKFwwTI9FGsBK53X4vVPzGI/vza+5JzU+//TSNshvsdD0x1zcJlCXSCilMKqTnro6jTxoxHNj8P+fgtRFEWuAXMpn1JkQKSjBQ9l25TsnRsfIgZzeyOmSBUwAyhdBnpcgsG1tP25RvJnCITE/ZvB8o00zMVzbXSALwpKHsWZ5cqHPb7nG9NoHl2OIE956YZXBokTsPtPM5MWOhM1SAcJsVI+mFCmud6N90RdMeyBwQ0U9EmuGOFu3WITk61+PBUzP5QC3AbK2MkoLBSpJuqFzZ1VjL6iDCTtWw1/ic0nYv6aWxrZbyfrH4d7PZVMD/d1VW9BKbNglh3KfkV0mM4+l64vxqfv9a2k/Z0wippztHy8iBNUqa5YFCW8lCz2O+VyYxjoWh7ptNEWIZvHhl6I8NJH7qxNxYsEqMxFrBXTcuUfXdrt1Y6MeCQazohD7GwoGpYX5flAgiAWVlQbpyUz9xkN6KZwg8m5N2gkCnJJ9SkPYSRnMrFoEnDUq621cG4yWmWpBwz6ElDkwPNshtCxwkO4sSWVCbKrvziCx56dA503rey1oZSjypONsNKPsabUUhKKRABWVGs0WpyUJA2nyeytKLPWSimKnEeWa5HqouIOcFHCO/zY5/hayreJzsZ/aZWX9/xm0ogIWez+rA44aZIROBGWV5Aq6bHHDPoQUMcqyk2osUUtgNQBWX8zluu0zgzzFbOGRnnGowZe+LxVEcecKwFjkUZqOcMBGE6bEyQI7E4j7DE2m277gXJfunaxy7sOoYQoSl4jswSGINK4OIC50BP/SWw6+KOaVtu7r2jSrY+sDhF3MhL7UZazh29iHOd04yjPuU/SqV4HoudoeshXH+uMRInl6qcmimT+YWjXHlImMFFc/kzvKpxVruXNcKAIH11ovdfZtZYuS6YOV6CnceaDNVjhEIVoceF9aCtIxmWOz5+Eqn/SmJkpa9E0N6ocdy389LPb1EUfaTMdieNoJQC6wRSGHG5lay83KIMZeZFANt1dfcvKOHTEtdRozktruhKmQy7nntgc/KwGeuGjG/5lNSjtk7c6brr8Nz7QpHZvvYAkWOxfXkpIBBIvPB06Jl4AS77m8l3ZrPdUo0SglSWGLtCG8vBToo/l6EsGcDtJcDOOQEt4xKrCYtz2WDzyqFoCcpzRPS0QMFWd/SijQQGO65aYmLayXMupLqZkCVrARtEDlib2XgM4gVO2oh890gHSJ21h74eNLwR1/by7luCW0E9xxeZMfhpTxjywA54D7D/djDVxJjDLsbFZdlJ4bYGHylUtADWCvRwN5GlR+/8+jLJmnxc588AsRXfNy2vfy21R7T84x/z6eB37tKa3pJ7GvPf5ZnF58g8BRKeoRJyOqgxW27PR59vjL2WDc42absOzqWUMu0uGnxpCXWgqcWJri/4FwTI8em6KUQeFJgjOHZdqOAdNto2UbfQbudY+pFkkFSyiWva8EIOdeLJHcdbI/KgKGi7QV0o/G3tz3wKSmXASkhUmfjIMBfPT/Bo+cafPC185R8w3QlyZF2jvHA5uUZAJliyibTx/me89jaQtVLWB248iRinOPNAs+vlvnYY/soe3Yd6s+ZJ12j/1MnZvi2fas0SjpVSRX0Ex8w1HzDct9z8HVhNyVLLSZLGYKxkp7bytDN5vQij32NoeP52yw4rYtOghE0O7Mr6klZiMyI8DVjYci+UFJCIGD/dMha6PpzxWCcWT0wLCkwhcK6ReTME8XrOCpBu+NkNEZKGAJlma64weSirYUe57ql/DifTElZ37C3gzZyhLQTcHqlwVytymv2TOFLibGWpX5E4EkiLSl5o7UIIShJQT9JtkEP27Yl22rGdGfhdwt0Wq3WylVYz1U3B254nMHgUSDCLwwHxcYwWxF4cg9x4Yu4Fnmc7ZSo+CZ3kNlOOTbw249ej6cqeEpT8xSTZY9OmPCF53dSDRRHZntcP+Wx0LN85WKZTz3jtG0uZbZwr8p3vnLMIa53SOvLgEX5gswE8MnjcwgBt+xco+wZ1kJvrPxzbLHOu29cGklkIByMOO1D9GOPXqR4dqXK2/e3qQY6J2+V6dCoSmmTBokcOxfSIz61WGOYeAzX8Xduhv67sFaiF8VI6Xb+05WESppN7KpHxMZlG4E/kuhYb1l/B1xAKHuaTx6fxVjBnQfaOZrsUmi94nFgBM3PHr8+oBVLfVly6qV9wcRIfDlihigCVzzhAqdDao4fVOT/bbRRaXgUDIoZ+4jGyH1uEmOp+QmmMiqxZu/L+k3C3z4zh7Fi7D052Z7grQffwb/6rkNIIfJAs9QL+f0vHOdXPvsUxliUNNR8zVqsmKpUmKtd26CH7f7SS2dXkr347y5zH61W6w9e/CVdXfvoZ47xqdaz3HUgxCDpxwmRNgRKUvIUkyXDTMVyvjd6TjH7Ge8ZWY4vTRF4VW7eNclTF1cRkNOuxNrSDudotRU/877b+b1Hz3LvM48z1FsX/62t2/ludl/mkIplwKw0VgRTFAPQfSdnx3pZmT14app3HGhjrCvPZSWc9sABOv74a3tYHbqyz90H245BICtrZSgz6fpLD5ycgRTsUQ8ShonkyYWJDWW7zN57aCkPplmpSmIJtWImSJirRnhqFGQy2HIGm79cXS0TxIs1XOx5PHxmmrVIcXSuR6AMtZKmfCn4XGqW0TwauD5XkX28CJBYz6wu0nUKXG+MAnii2MMaxCplDB9H24F17BkbK5f5XNl6e26lzC0719K+4eg4q6FjFC8rx5pR3JxsPGeHAvz0szPsqkuHDLSS111v+Kl//+gG5dmffe9r+cLzi0jzBDfOrFELEmLtsziY4aadN16zoIdte2ntShnT+9Kfc8BtwBdwQKE3A5/HDd5eM5aJk8UmINQ+ntLEJoMdWwIg1D5Hdu1k6dmlFPXmHMe9J2YRQLPg6FuLNb54dieztYBASSbLvssc0u1yyXMDju+8cQ+z9VmMmcfLeOy2uOZYe5fuVV3CIYFzKA+cmuFL8y5Irg79sQC0vpeVWS0wDGLFWuSNZYfgykM137A6hLLnkH61oPia7j9jBSuhz/2nZtHGsT/csnONim84MtvH2KUNA5pZTwTc3M2oWW/wlSZO0pmmlDmjWKbzioGBzeOTIM2ohAuaa5Fy5xMYlgYBZX8I6vLvShZ4vbS8aQxEiUwDlSVQTgU36x+BC0ZhItP1WHqRpOxbPGEpyWK4cP2+pX6AtTEIQ9kDJUwO4ki0ZM/EkGGS9Q43ZjrjWaeTxfCVzodn+4mXS114YtRTulxpGUAKn27oHhMmmnufPk8t8DdVnv3n7/d5+GRCuy/pxwolNXvrC8yvPMZHHvS2pdO37Yp2JdmL7wNoNpt/BhxqtVqD9O8JHAvENWVFcbILa1PsbSy4nS0ZP5vlwtpUSunicXRXA2NgqT/kfHfI/ad2cP+pGSZLmn6sqJfKByrxewAAIABJREFU7G2UmawE1EseH3rbERCCz2zClpx9mTWu16C3GJl21ys8v1qmOecGVjPLHJLexKFsVhIrZkujx20MkMUSUDE7nK7ElJXhg687x1rk8fRSlQtrvoNzFwKDMYJQS0rK9Ynu2LfKbbu7WASxkZR9kyuifurEXH78epCwrxHmfbyMpNYN6IKVo97MelBC0S4HRgCXWc1UnChemAiGiSBQZgwKvplpM4KIS+n6bYkRaWnQsDLwONcJuGEqREtDkLYhEyPppWwaUriADrieXHZs67gHu5HjKDzbKfH0UpUjcz2uawwpeYZ+7AOWyZKmUo5plGIWewEPPTc9lumMZ52STiiplxKGiRoDwrj32ttSUBKQ94wsIKWg4o+7jkx59kffdpiFzkn2T9eBHro3RAiRbgyW+OsnTwOvPkqibXtxbas9pgNZUAJotVrdZrN54Cqt6apZUZzsqaX9GKDmL1DxEgaJR7szx9NL+/EVVAJFxfeQQlAv+ygpaQ8iwkhw+769vPXgDn723beipNzAjPwTm7AlX+j26YYxvnRywFlf41KWBZfbdg8IVEzZM4ALiL3IlV7uOzHLXFmxGhviNNJZxp2TQWwaDLKqlWR8SG09aAMyqQXHdJFYmXKpdelHkiiRjj2gEOX6sWQt9Bgm4pKKqJmUd+YU33zdKuWUPw/IA1N2Uhkh6pX22ZcLStnlLnmWn7vzJJ5Mh3GtIfOzm0HBM20msAiT9QBHK/Gk6+P0Y8dCXhYObi6tCzrLA59pXJ8n8AzGuL6dSBnMJW6YNYPOP7VYS8tn03zoTc/jS9I5MOPmiDSpLL07xmQ5yTPn9dfbAv3YS+eRitdp857Spa6bNo5OqJYKgq2XRAfH6HC+s8Iw7iOEYnUYjT2upGIqXnJNURJt95VeHttqYHqi2Wx+Dle+M8BbuAa58taLk7WWDnBuZYZ+3KMa1Ng7mVHyW968f47VYcocDlw/VWNPo8LRmuWj3/fusS/VepRRxpZctKmKTyeMGCZby5ay4FLyPDcIGbsR/WMLNT5+fEcqA+6c6GQ5YGUQYa0FMWKqyCDO7ow2BgMpTc48UXRQxf5UvZRQVialNxp9XCwuWzixXOHQ7AAFBUixC5xlz26pP+ZJV+LLCWHTHTa4PpIjm3WwZHUZX2bMOAfeBsuCjnA6QyaV2NOIvF9lwPHNFbLA0SyTZBBbtPWoFBGLOBj7ZMXgq0zh1WKMRAnLTCWmE7rZrumyQWMJE8kgLfGVPSd5P0wcujO7/mXPCSBqK9b1idxQ9VRV8/7DS7xhT4e1yOPZlTL1ICGx48GmPfCRFQdHL3mWXqR4eqnKI2cbeNJcEhVZ7D8emKmxf6rGuw7t4rMnL9CLxgOdsZaq7zNdnaDsV1kLB8TajAWmUPuE2mcYblMSbdvlbauB6X8A3gu8Fvd9/WXgk1drUVfT1ouT3bRzCimmMRbag1H57cN3Nvn1h1obRMzeXht8Qzu93/xciwudAdF6rPEmVuy3xMYgcDtjKxSHZiMmTntoLZitlYnjiFLgs9QLkVIwmXKqJVawPv5lwWB1OGKZmChpuuHGwcysP9UoJXzPa85vcHbueIbfeuQ63nTd6qYoPyVtXhZ0pZxRz6rYH8ugzSsDD4l1cGlF2q+CbqSo+ZbYjOZ+MsvKb5aRBlEWTDZAuNO/R4HGPVtYaA8Uk+Uk1bMasXaDW4MnLYNYUPbAhRbXYwo8dxgpoKxGA9ju0BZPwEwlph5oDJZQC86vjWD/bllO1+qPv7aHpf4ItZaVVeslB4kvvp9SOmqs7LzKvuGWHa6P1402vldnOyU+9tg+Kp7hTdetcmS2zxv2dMfKvMCGEnBrscZnn53jt7/7Ldx+3SwlT+FJkW/uLHBmpUe7HzJR8vn+f/sw7z9cZf/kEF9JdF4WcGVyYyUzVe+aRudt29W3K6Hybk/JWu/GVXy+Urj7LuD+q7e0q2OXEicLE72h/LbZ4x577LEX/Jphovl/HjlBYiy+VAhMqlVkMWzcoRZnUFwcG0leVPyEybJFUiGxhqVBzK1TE5ztSGJtWB3KK4IliqW+2IyX+u49MTvunGJJxdd0I5dqOOZqF2B6keP/++QzOzZF+SVG0FqsjTFXaCsYxIKHF6YK63Kv0Sjr9DFu8FamkhnL/QBTSaj6MIgtJc+OSURkc17z3RIHpgZ4MmUslzaXth+DrAtHN2StRRtLkp+ZK6tlZdbEwFqoUrJTy/IgYLqSMFWO3WukhLMOEOF0lorNKmtHcuzWWvqxR8VPmCjpDfRO3eFoMHZ0/VxZ9Q17Omgrcr5AIawjuMV9PrI5MUvGbm7QhY1EEaL/jgOLec8vQz5m7z2wARV5+54OOybK3H6dC1znVvt8/5sOsTqMefT5JY5dWKUTxkxXSlw3VWUtTPjzxxv8V6+ByXJMu98l0j4X1qZc+fwapyTatpfGrpQxfR/wZeAXNrnPcg0GpszWl9s2K79d7vYXYudW+8yvDpBCUPacAqvb0VvetO88t+zqU/U13aFzRA+eclDmip9OngiBQOApgaCMJ8ssD2JWBxHGwhPnVzDWOXTsxh4RjJwTbOxDwKjUJ4Udd1yexVOWmUqEReZDt9oIji9Vc6d4KZQf2StZC8KRuPoS3rq/zf6pIU8t1tKMwSCFIePgc/BqQSdUSOEk1J9eqlJSCe8+1KZSIK+NDXSGrlS22POZLCdY65iz/QL3XFbGg9EMkUxRdF5ZE2oX3KRwzOdfmp/g4TNT6dCxC20rA8VkOc7fv1xTq1D6y1Roi6XAXizTXpMj/13BI8NnzlRi4kDyI288uwGokmUy7wjazNWiXONJCRDG0I48LOkAredY3MExm7h+5Oh46wmJi+/QzXNrTkplfSFUSN52fcxvPPQEDzyzyOPnVxjECRVP0dzRwFOCW3dNjqHshJB88pk5/vCD38nvPvw4D59cZbEXM1P1rwnp9O2+0stvV0Ll/VT68+7i7c1mU7ZarSvXpLbNWbatTvftmVN854EFbtnZxVOOa69S2L22Mt0dIR1rhBIkxnBqucbFtTgn5QSItGNwzuxyM0yT5eSSfZ96kHDLzrUNzqk9CNhdH+LJBCkE2jqphVqgee+hpTF0XdE8aWjO9VkeBAgss9WYaqAhDXgVX/OGPR1qQcLywCHGioGvEyr+1UMHmK4YLvacYusv33Ocij+C3Ds0pQAMxxYmODA5SBnNDVGsOL5Y4uRKhVt3rHFwZpgLKxZNStdXCrWTcM/W8J4b23jKUgt0XspU0iEPo7TH1osEU+VRv6WICciClQEaZU17YDb0e6p+goWcpWM9UCWbIwpkwvtvWiJQo0Fi91qOxSETa0ys41AUGJ5cqPGpE7OUPZuLPK5/77MMOCi7axpqVbgPfCWxRHyq9SzPLGlWBhECGEYJT1xYdeSsBq6fro1d0+V+SDe0/ORdd/ChOzdWI7Zt2y5nW6Uk+n6gCvwW8Gng+maz+cutVus3r+LaXhWmTcJUOeH6yYDTq2Hu8pU0HJpdo+R77J6osDKI6EUJ2lhu3tHj33z1Bkqe5PBsj8mywRLwzFKFj7emCHU2xZ8lAG4Oq1gS3IwQFi7P4TdMuefidc1wgcWTcKFXTtVWR/2r9YCKohUdoSuBJSkVj83598A59rVUOyrTddIGZioJP/Rt5ygr16vylSMOzUwKUCmL9856wq271oi1YikNhEpaWks1vnBmitfv7l4RsRdIgwps3mfzleXWnT3KSud9G2NS3jtp0UayOvRplHXe25KWnE0cRuATX8JsNWGxH+T9nlqg+d7XzVP1NCWlibXAjRSPgCoZZ90Hmi4oZQzyWBegJkuaJB3YlsBanLFtCO7Yt8oN0wPqvpvbenqpylo6R5UFtCwIx1qyFklHt4VApRudiq9oDxxwYWUwAGsZJobEGnrdAULA2U6f2VpAOUWxwri0xYtRddi2by3bKvjhR3E9pe8CHsfJXtwPvKDA1Gw2PeB3gUPpa/90q9V6qNls3pYeywJfa7VaH0of/zPAd6e3/2Kr1fr4C3m9l9PWk8R+6C2az5yU3HtilsjAVOB6Krsn6uyfrnHdVJUoMVzoDpjv9Kj5hodO7+apRY+Dsz69SHG+EyJFSNV3gWEtilPVW8N7Di2lDAvjc0srw3E+tM3g4OCCz5MLExyZ7W8IWhkQINGXBlRsVsYbY2YvjaTOBa4UNVFKWB24+RwlDYlxekOJcRIdZd+V/RIr2NsIc0bw7Bj52oU73mRJs9RXecaVGEfb9LnTk0R63ZPWmRQOyKCtJUzcA918kQRlkGgmKybnBXSyEZqdNReUsE62AkQO9sjWma15spygreXB+TmGiYNwH57pU1I2VxMeJJKznYBaYPM5sDfu6+QyHUK4L062KSl5IwCGtk7gj0oMWCYCzSDxSNI+4m27u/Qi1+OcKmRZIBhqmZZs4zTDddG1UfL40rkJBrFjMslIWoVwcioAw8Tw5bNtJso+0+WAvZOV7T7Sq9yuti/f6vj1oNVqhcB3AH+alvG2OCI6Zt8H9Fqt1p3ADwK/kt7+q8D/2Gq13g5MNpvNDzSbzYPA9+B4+v4O8Cup3MY1YcfOPsTp5WPEOkJJj/1TAXfdGPI9r1uj5ikGsccg9lgdRDzf7jnYsK/YP10j8CoMEo9YG852hjx0ao2vnesw3x2gTaZgO+qdvOfQEq/f03FM24K8JPjeQ0ubru3eE7N8eb7BMHZw5mEs+fJ8g799Zi7v+RTNGks33PzS92KJkibvbRQtY2av+tphAgqH1dYFkj0TIZ6y7K7FTKcONVNQ7ccq75/US0mu+rop2M5CvZSwtzFk70TI3kbIdCVmXyPkh994lplKwhWEbQEX4EqeCyyZxEM/VoSJouprlHQzTYlxYIeS50Aejjsvy1/T61a8hnYUkKUwTJVjPvj6ecq+zRkplHRIxwNTQ3qRcnNgO9Zyqfbs2EK4Oan1QdoFV0s9iNMgKNcR6Lr+3RMXa1Q8jRBO/K8bOfmR9iAg1pIwdkzzWI+nl2b4yycbHF/sEGtNbN37rI0D7mSjD4mxRImmPQiZqgSv+D7SpWy7v7Rlu6q+fMuyF81m89eBtwM/3Gw23wqUX/CpwB8C/zb9fQGYbTabAXCw1Wo9kt7+Vzho+h7gE61WKwIWms3mc8AtwNe/gdd9SU2bhPOdk4gNQ4iCerBIP6mgpMfplQaT5Q6L/RBwdfozKz3OdCaZKlc41+0TxgYpBRMlD2EkwzhhMBwJB7o+To+5qibw9Bi/3aXKbFnfYrNS33h/KnFBBafLtLcxHJNfn65EJFps2rTP7ItnJrn74DJlz6BT4bvMmfnKYhB5Vlf1NRLLhbWAYZJpNcFUOUGJkaT8est6Tb4EY2yOTpsuJxgsywOPpUFAxR+MUShtdhxBWhKLRufZiyXCwrlOiZlK7IhWlc2D7emVIM+mhDBEiQscXkr2aozL+rR2M073HF7mDXs77J8MxzIq0tcvKcBq3ndoicMzg1FPKXsA49cho0rCusCncTRNnThD+o1g+vsaIWUvwU9vGxQ0mcBlbH/19A5umttJqMsI4TFT7bHYG2KszYmEx6Dr6WtPlgMOzU1gcYFKbbLt3Qz9um3XpF1VX77VwPRB4O8D/2er1dLNZvMG4Mde6Jm0Wq2YkSDKTwJ/jOPhaxcedhF3Iku4E15/+2UD0+OPP/5Cl/WC7UqQ8dgOWAwXkGL0zbzQi1kJNUpoal5MJ4S/PtYg0Zqjcz2WTY+GFHz5+RKPnZmmoTQLAqQnEQJ02kdIzLhTqAeafY2Qij/qO0nhyjiyEV6yzOaOtRFJVwxa33HTAjfvcJo+oJjG9SRkxWUNgqxpvzm7BOn9ZztlKr7rZUyU3DEqvkO0rYWjALAy8ImNU8X9gdvnKfuGmWqcy7+zrn+TWaaRlCG1Ic0oxciRTleSXE9ps3JgBg/PCGl7scqP81y7yq07e0yUE6qBzil2EI63b7qSuL5WxVILoJ84gtR8ONi6DpQQ7v0CgZ/Kza/Xjcp+39sIqQU2X/N6jkAYP2eR/RQu+CXWvd7eRpiLSGaPl8JB4KV067G4IdyMdup7X3ceRZvnVxt8/dwuJhUMPQdND/X4WosBszsM6XZhpQMP/KdH2FEdBTxtLH/SWuaxCz1Wo4TJwOONu2p8T3Mm72dtZt/IeMY3Yy/1612rdrV9+VYVbOebzeYzwD3A08AXgZOXe06z2fwh4IfW3fxPW63WJ5vN5oeBNwDfCexY95hLfUov17vO7TWveQ2l0tUb3nvsscd44xvfeNnHaJPQe+oZYh0Bbip+sLaCVIp+KAhNgKckoLj/2V0shg2kCPmf73oDX3nsceoTHmGisZ0E38syAstk2acTOWYolXqDSAsCz4wFK0ib+cqkfY9vzA5MDSl2SzJwQmzAaEEw3r4aa9qvn8VxzOyS9iCgKw37GiHd0GN5bJ4HSsriSZHP75Q9PQa9FoXgZHF9r8QKSsrQi92wbMlzvHROxh32NYZpMJJYa3KYeS7klwYlxw3oSqTGjEQMHzw1zQ1TA3bV00bVaFQJax1Dw0wlphZojJUs9gL2NkI8YVOmCsconjlwnUp1ZEPA2RqKn/CZqkFITT9RTJfjzSU9CoHY4AhlbZrZ7ayH1PwYT6bzT+k1i1KARcayYXGsEoKYeko7Vfbd9+fwjg7lIODY0gEaDfcZPH5xlU6UIIVkEDvdEmtT9J6QlKs1Zmsl7n7rHWMZ0UcefIIvr1q8So3ZVO7sy6uW63uVS/LmbeW7llkYhi/KpnSrr/dqsitdu5fDl28VlfcvgJuAA8D/BXwvsBP4B5d6TqvV+h02IXptNps/iDuJv9dqteJms7kAFPn29wHn0n/NTW5/xZuSHrsbN3J6+RhCCGJtiLVziCfb9THi1VhbTi33GcSaf/bJx3muvcZE4LN3soKvBP1Ik5iRfk/20zlWS9WzRInACwzr99ORlpQ9u0H36HKWIfuUNJvCynNH5rvjr2cg3wwMsQG+HjpC0fVChjAaAr73xCwVz2WD2qQlKptmNKlDToxz8tpAAlxcC5yooNJgUnJW4QKHU2u1oz5VIWtyx3J7f23gqcUKf/nUbi72fIaJW+OplQpH5vrYNJoVVYyd7pAb0u3HkqmKI7bNBnNhXOtJSMt1jWgsUyr+TNL11Xydlz3XZ0qZIm5mJg2q/VghhGMxl2UIxCgQuwwvAzi488pmn8qezWmnbt83wemVHomx7Ki1ObZ0HaCQQnDbvhmeXe6x0BsSa4cG9ZUTBlTSofky4ENWtquXPB48cWEDv15G/Hqt8OZ9q9rL4cu3Wsp7V6vVekuz2XwgXej/mnLnvSBrNps34kqA72q1WsP0WHGz2Xyq2Wze2Wq1HgL+C+DXcJnZTzWbzX+KSxH3AU++0Nd8uezmfU5b8XznJInuAwFnVxs8cXEWS5Q7Gm0My72QnfUKtcBjouSzsDZ0d1rXUBZSOEHDgmW7/dVQcbZTYk8jGpsB6ice853gkrIY620DI3ksqfhJXqorWjdUzFUj5mrxBs2mzaQ4NutpvefGpQ3KqOuJRT9+fAc3TA2olbQrAaYKrC7rEDzXLqdM38KVyIyl5hsyOXJjnUdW0jE6UDiTjJbIpH0ZX1kGkaQTOsDFB183n/fN7jsx42TPhaWkHDWQNu6cITu2k6RfGfjsbYR5sFPSbkoMK6XbUGTZDGIEBY91xvlnqXhOITk7jivtuedlFExh4spySliqvk4Z2W06m5VyJmIpKbeOaqBTsIPLgK11lEixce/b1+dXCJQk0gYhDM8uL6FEjb2TFd57ZB8Af/n48yTGcrE7ZHUYEWnD3okKf+811/PhO5t85MEnePDEBZZ6IbXA4/hilxtm6hu2ysv9bd68a9Guti/famDKmMVtuij1Ap5btB/CRdSPN5t5AL0HV6P8rWazKYEvtFqte9PX+RjwmfR1P3QtDfVKIbn1undy1LyNMO5zfOVZnjg5z74pgaXHyiAiSlyhbKZaYu+kq29cN+UGFTvDCG0sZT+bK5F4SuAJt6POiE5jI3lqsU7F7+QzQOB20E8t1i/JHi1xJK6bKd6OMz44+HBmAje/4ytbYGpwPS2wPDA/e8nXTIzMefqOzPaYLCUEymVeZzulDWJ1iZEcW6xz+54O7UHAygBmKhG1QNONPAwpSzeWhZ7PnomQkp+hxgQrQw+LZaaSbODXA+fkXa/Mcq5T4msXJtzcks8YJc+RWZfpdUKfiZRkFutQewLQRtIoGSq+QYg457XzZEYhlF+8HEHo/hYME5f5+cIiUiolgyDAEKTsFopRcHMEsY4J5EIvoDOUPLdS4ea5NYQcydjvmxjie5bEjEKytiZFBboRgMS4z9DTSzWunxrmkhyJMWhj8JRkmHishhJhQ27ZPTmGtnNZEDR3Nvi262f52XffSjXw+ciDT/CXjz+PTrOpSBu6YcyZlR7XT40P4hbnnbbtmrKr6su3Glw+32w2fw/Y22w2fwr4L4EHX9h5QKvV+nng5ze560ngHZs8/tdwEfeaNSU9qqUG//CdrwEUD564wK6JMjftmKAfJnzl7DLLg5hutMp0OeC6qSrXT9XoDH1iY5muBK5HFSWUfMXxiysYhvQjRaidE7r/5CxSwNv3t5ksJwhgZagQKfR6PZODwPKeddnRjmo0VloTCNqDgIkgIYqhXjZ0h4rWUp0js71NmRoSLXjw1PRlr0eRpy8bhPWk4fRqiQdOzWxY6/oy4NlOKc0YyFktZqsheycSl1UYl6Fl5cX2IEBA3k8pj+kguTPVxkWMI7P9Da8PcGhmwPm1Us6unp1zI9B0I0UnDPCkC5g1f6QlpaRNwSijY2UlNWFBa+gnHlIYhDL4WHxpUalgYdYX8tIypjEQp4FHW0GcCFqLdY7M9tEo12hKrZcoZvxkDFChjSDUApuW/bLNxM07elR8jacs7XQT4vpell68g6M7ZvCVK9udbvfYO1ndlEcSoB/F/O4Xn+FCd0iUGAJPMl0OmCr5tPsh+yareUlvmzfv2rWr7cu3Gph+BUfk2gOuA/41jkNv27Zo68lj/+jRE/z1sbP4nnIzIcY6SC6W3RNV5mplpIDjC13aw4hEG+66YYE7XtdloqyRsszplQZ/8tUaiZUY64hMB0mQM1e/fk83L6MVbb2EeT0wTFcSPEWOkHPMCYaS0pR8zwU4QS4AmKT6QSOmhixYGFaGmzuajVxtlqmUfWDXjSl33kJ9DHJ+KWi7Jw2T5Zh33rDModleynQtMJgcxVf1NQKnWKutzG/PSmYZ2KGfeAwTRdkzG1jUlXT0RlmGMTpnw556jBAiRb45FF42LOUyKTZCpsUIbDHfLTFZSXI2i6IQYl62y45lHR9gxXNr6Qw9vjTf4NGzDd6wp7uhF9ge+I59XFgkIi23KlYGHl89P0GgHBt5Jj7ZjQQzqe5WxrF3sj1BO7yOwJOcWemx3I/4+7//afZMVnPOu/UluH9x3+OcWOymVFEwTDS9MGHvZIVG2SdQin4cj4lovlJse4bplWNXYhd/B/AnuJmlBeDvtFqtZ5rN5k8AH8UFqW17AVbyFLO1Eg89u4AnJVOVgMVemJaVLGdW+ix0h1w/U0MAC70hUgjuumGBW3etoA0YK6kow/7JRe4+OOD+U7McnethkDk8WgqBtRtRcpsReWrj+O8cuahiuqKp+C4oOVnvhJWhT80n311nFD0ZUwO4wJj1lzbT9FnP1VbkeHNZzeaQcxiHtgssdx9c5pa5LofnBgSezbOJEbLO9YMcEMDLZ6L2NQZ4KWjBGEE/UawMFE9erI+xXmSURsYURALJxn9dU8hThppw/RuVMpmLFHUXGndWWW/IXav0fuuyNKVgqhzn5ycxKUN5VrYjB2oE0smsn1wuc2qlyt8cnyM2Ck8aepGkVtJjUhogOLFU4flumYPTQ0pqJEny4KlpfuyOM2OKyCBYHgSEseCPvr6H1aEPVrJ/egjCqT8rKaiV/A1S6pmFiebPv3o653DMSpiJtVzoDnnXoV388fe9g7Uw2Z5j2rbL2pUypv8NeG+r1TrWbDb/LqPaYRt401Vf3avUMon3wHON9lgbhonOIcV7GgFz1RJPnF/FVxIlHK+eEBKlRrvpxMDRHX2+Mt+gHmi0zbIMF5gMdgNKbjMiT4vrl0wESQp7ds5Zpj2Nqp9gLKwO/RQ+LpAYNpd6F7zv0OKmsu5Fnr6M3SGDI2TO2gJHd6zxpfkJVof+pv2q9x1a5O4bl6n5hpKXhgnpvHhsZNo7MWgjmV8rpYg8d55nOhUmgoSVgaLkOcBCtj5jl7h9zypTFZ2X66S0WCPYOzFEGwezbg88rBnNT3nS5vNP4LKksnClKpedpQFTuIAZa0cAGyauV5W9F4kVOec42ZVJQS4W6EQeraU6n3xmR37N7z64zFzKmqHTQNseKGYqCXEgqQUDepHk+GI9D2ZT5fiSRL7VwF23xEjKnmKpHxJrgy8F0+XSGLLub546xw++5SYaZVf+O9fpc747xJMyR6Fm5xEmmtv3zdAoB/njt23bLmVXCky61WodA2i1Wv+h2Wx+BMeJ9O+u/tJevZZJvB+7sMpSL8wbxLn7tIJIG2JjCZTgtr015qoWK5y0wzB2pR9jLdVAs6NeYZh4eePfGLeLl0LQjzz68WhneikS1/bAJ9EuawFHsaOtzZvpFV+zMnQfl36seHKhxg1Tww3s5evLhOsHb7OZJiVt2oNx2VJG/zNdian5mg/dcYbVobeBTcKThjsPtJkqO0BDVvoSAHI0gGyM438zmzjfQSz5d0/tohN6Y8Hv3hOzHJntsbcRpWKBLuuJAW0lQggmgoREw6NnJ5mqOLZ0VXiJHG1JFthHXIOxgdXIZ3WgeHKhzhfOTPKP33UqnbcyG4UNC5ZoQZR4HJntc99J995lA9DdyMNTlqpvmAgSSspJeGQ9w5JvuWXHGgApLbR1AAAgAElEQVSfOL7jskS+vUgxiBWTJYWSilgbIq3ZNeH4HK11WX17GBEnhu/+vc/w7Uf3uJJcOsFb8rIZNpODNgIl+e/vOHTpE3yZbbuM98qyKwWm9XObp7eD0jdvJU9x58GdfP7URQTk9XiT1nzOdvp0Q4fKi6xgZSCITECgXECqlTyaOyc5dn6FfmiZqU9zcmWRm+dWMNbBHTIpiFPtKQ7OTNGLYrrDmGGiL0ni+sjZSV67y8leGAN7JsK8R6JEyqRgnUDgJ467Xfv6vs/ltJ4eODWTgxlunlvD1F3pKYOaT1diJgKNTstnm7FJTJZjZmvxqHdTGEwVws12aSN4ZqmKEGKd83WM2mVl+G9ee55BInnyYp2/fcbJSyjphP3Odcp4yrKzFubBQliY7wZICWuR5G9PzHJgus/1jZBS+r7kwAbIg5VNh2kllmGimO/6uXy6EwUUTJR0PmhbtOyzYawbC7A4wMcHblrg4NSAw7MDDIJ+LOiGHqsDD6UsO6oRq4Psqz1iEd/bWOaG6QFPLdRpLVbzHmTxM9BK4frDxFINLJMVDwaWnfUyQgiebzt6IiEEgSeJtM7Lej9+51H2TFY4t9Kn7Eky3TGw7JuqbpDG2LZtu5RtlcQ1s/XfnW37Bu17bj/IRMlHyiyQjAYxIS3vxZq1KOGpi2t8Zb5EmDhanalyQKAkM9WAM6sTgOJzz+3kS/MTDBKBLy2RVjx+cZKhOcqjP/Ud/OQ7b2bfZBVPSu47MctXNiFx/Zvjc3RDx0YtpGSQOH3zbCbIWIEszBq5vo+/oYe0mWUlxQzM8BuP7Oe+EzPMd4K8/1P1DRZLP1Fj4Iejc72cJNZL5cozy1gTsg/mmdUSn3h6jl//4oENhLTTlYSJIEFK2FmPuXF6yAeOLPLjbzqNYKRXlB2zmAllZb3ECGq+4X2HlthRjXMqKG0Fho0IPG0FsRaEGnqhhyhkcHcdbDvWc8bia86vZywMEkF76OdouaqvuWXHGrWS4+ULlGa2knD95JD9UwP2NYbUAsO+RsR0xZX46uk5S1Hs44lNiXw/lW4cYm3oDBPmO0MGkaZ1scOzy12WB6FjFgemKgFSiHxQFuAH7jjETK2ElC7r9pRgtlbiB+44tN1T2rYt25Uyprc1m83Thb93pn8LwLZarf1Xb2mvbts5Uea1e6fpDGPCRPO1c23CxORIrH5scvRvYg0PnJwDC3dcF7NnsoSvAt5847dx7PSAiq9IDDz07C7+0/OaicASG59GqczRXSpHBP7o247wS/d+jd95+ASfO72L+0+5XkMvUrkGU2uxxuv3dABJJwzygDFMJLHxeHqxxr0nNoeEX6lEVBy8TYzkr1o7GSQOtt4oJ0hsqss0/rGslxL2Tvz/7L15lGXXdd73O+dOb6x6NXb1jEYP1UBjJMBBJAiAJETSsSMPsS0ptGIlkpYsyaYlZUleyuT4D2dlJVlSREWytSzJkikqTiRnyXbCEQAbDUAUSIAgQQDdhZ7R3dXdNb75vTucc/LHuffVq6mrADTABvi+tRpVeMO999136+y79/7294XMNgISI0gMeGuCE2nP7Q++vZu5ltUX7qeal3ybKalUqSEbwpUCDo+1+eShBZ44N9Y7fksIsTNakBJE0rJmFhxsCQ2GgxjPWWH8ZaFQG4ErrXZhJxZ0EkngGe7fWUcKw5GxNvXQZSSvcKAnQ6ENLHdcyr7iejPoBf5skFcjMdr0JJeAdQaIQmCDsDA9m/X+Pt70eIt/8a29PbZjO3KI0v1k5AVtTK9M2o4VzShGa6jkPUYKQW/mDlYGZX/xkTvR2vDVmatUuxE7ynk+cXjqlmLfDXDrY6vANL3F8wO8SQSuw6MHd/AfXrmMQPTKeNmSntGEpRA4QjKSD1iOpvnmVYd/8MgDDOXKONLlM3e8wOhtR/jRP3qKYmCp3rHSeI51vl3uRL3J+oLv8c8+fT/fna3y7Pl5uomk3pW9u/XAkZy4MIHnSI6Mtyh4CddbeV5bKPCN18tUCkOpUkN7w890I6+nfkWHDP1U8OFcvMrCPEMmLPqZe2ZpRi6vLRZYbHlMFONUHWFFomeh5a0aBu7f/q5yl1/44CUqvuqZFWpth1UdaXswT5wbW3X8lhCi0izO0hL6gwPY3ly1Y5UwhnMalb5Gp4QHP7XQ6KrVWeCdE03ynrY+VfTp5ZHKFwnBUsejFToUfE0rcrhYzXFsstljDPZXTHuyQ73t2OaOKw1K2U5e1seDfukoj2pX9jaV9cVWzqG9BnOugxAOrTCmHHhMlnIYY3oK+qOFgEre43MnTvLMhXlasWK0kOORlBLuyDdanHnnMOgv3XrYylr94jt1ID+IyO4if+8vz5Bog5R2dREIlFkZsnSkoBFZWvFCK6YTB4wUVr66XcMFdg4XaIZWFC9wHbQxhImikvdXTdZ/7sRJGmHCeDEgVpowsQy5cuBx59Qw37m8zNfPT3D8wih5N6GTuESJwHMkuytDaK253ujSVStZkWCFhHAjW/fNkGjJYjvoqTxkC/hIOlfTiFwSI3tmd9ebPp5jyLt2ZinRNht55uLIhiy+REtLc3dXM8UyUkKYCHKupbf3H39duCQpVbwdO3Rj2QsOq2HLbTk3Yq4dUPQs3T5Tfog1LLVX/6nlXCuwO1nUVgNwTQaYcxO+fm6CJ86N9fp4AsODu2sMBXYY1u0LRqssQUQ2P2XSuSlBs8+uBNZnsFn2KNMIl327jhS97xdjy7qXa20WWiG+KxnNBz1jwN/9i9f4D69cRgqRZvGaL568givFpkKtAwywEd6MrNAANwmOlPz8Q0d54vQ1OoliqR3SjlRPGcAATiY9pAyx0j1GXz9WZ1+sYk3trRT4nWdO8dmH7yDRhuNnr+MIwd5KkV3DBV5fbtEIY4wxLLVCAk+itEEKj1h7dmpHKKbKeWZrbaqdaCVoAkXfxZHWzbQceMw1Ojx5dpynzo9SWDPHtBVWld4CW3prrCntZWZ3x8+PMj3WopxTNLoOJxdKmwY/V2qOjLVpxdKaAPYFASkNncSlGbk0I2ddFgd2Ec+5preQ769003LlantyARS9xDrAdsB1NFPFkFbsYMdcTW8YuRm5vF4LODDS7c1eZXNQiYZu7PDNy8Or5rd++OACrmO1/0yfKK3SQJ+oqzH0yo7dRDDX8FEbUPvXfi+etAaIQrDKVNGVNqPqxKqXTXUTRVcpurHijh1D/OyHj/Djn396INQ6wE3BIDDdRLwZE7TFVki1E3HbaAlHCC4lrRXPIAN+2kTwnNXKzWuRZV+//80zzLW6+I5kspxjopzvsaZ+9P4DLLbC3vsdITgwWkIbQzNMGM557CjnVwKbMviuxBhLzlhsR6mSgiSJVG8Gy3ddyr6VU7IagAplnE19oDbD6tJbyGfumV2nxABWXeK5y5VV2cSNgl9GaljqeOQ8TcFdUdzWBuqhXLVQZ/NBG81iGUSv3LfWnrwe2QW87FsNQN+xWoJ5N2HXkEYYcKRGa8HpxQJfem2cB3Y1GMqplIEJ3USy1PboxM4qiShXao5ONGmEHoKEvKd7Yq4aW8rMhnO1ySgfhmbo8uK1IY6MtTfNYCVp6VgIHGH17TwpacYJrrTK4cYYojQqOVJQ9N1eyfDsUpPrje6qa6sfA6HWAd4oBoHpJkBpzedOnOypKY8VV+RWtqqtZxlQM0zYl9Jp51uWppwNNsbasDNVbt6sidyffVVyAZ4jVqy+07vWn/rQ4d6++iGFYLQQ0Agj8p7L3pEiu02h16u6sNRkubOiiC6lJHB0L4AaY1juRNS7oZ1ZcSVGWMuON4NES2YbAc3I7VNioJdtZGWojYwON0I/KWO2HthZKVchpSFWljLer/G31SzW42fHkMLwqUOLadl1hfIOUPZj6qEk0bYvtWuoS8FbcXjU0nBorMPPPHiFZy6O8L6dNVwHYmUFXNdmNALDXzk8z6HRDlJYEkMnllxv+gwFCUXPkhzidF5Za/uvndgZti2DeHoTpLRmopTj7p0VHtw7wslr13n6XI1Qr9B3pRS9DEqkE8DX6h1CpTa8tuDWFmod9JduTQwC003A506c7NXWA9fZVLJlI/SX4aQQ7B0pIgQst0NGSgF37KisUm6+EbLsa7O71maYrNpXBm0Mjx2Z4pnzc72FJfssAIfHy7y20KAVJcTK4DmCXUMFqt2IWjfGkxLPlZR8j2uNDokyrOflvTH0Eykq+czywlo9nF4o9EpVb3RbBitOW0Uzmo+JlMOdE232DV/uyfVsNYuVaMlzlys8sMtur9+PSgDDOUWn6aTDw9Z+AmOtLDJzwMA1HB5vIYWh4GuGc/a817oOz1wcWZXRPHZwkTsnmpbxJyzLL6O1L3U8rjV9HDTZ177ikSXoxnLLIN6TEEKwv1Lk9uELuPo7fPKg4BO3B7w6V+AvXp/ke9eqvQxqNcSq6xhs9QCsgeBAqHWAN4pBYHqLCBP1lk3Qsizo+NnrLLVD7tgxzEMHJvmx+w8wWc5t+4+6P/tai+yude2++sU0XSnWBa1IKe7dVSHWBm1snwtsOWe5E1FOh30D1+FytUWszE0bduspMZStPXgmuVP0Ex47uLhOT2+rbcEKKaPgKTSiVy7LsqK8u16yKUO/vFMzcmiE7jpqfNZrygKnIw2+q1cxCLPsLy/g0HibK7U8zcjtafOt6PGtaBv2u86STmcVPE2tY3h1rgywkuWl+96sl7QZYm3YP3yePcN2zqnaFRyb8tlbifkbd/v83P9TYr4VrgrZBthVzrFrqMAvPDTN8TPX+PqZ63QTy34cLQSo1EbjVmbmDXBrYRCY3iIy3bu3Ultfqzz+ZgUu12ZfGdbaC2y2r/6gtdDscm6hwXI35rmLC6iUnZxzJcoYtIFEG/KOZKEZMjWUY6kd3bSgBKwoMTSCVVkAsE6cdiusJTX85/dcXWWBkb1m/4jVlst5et0++5lsm1HjtbaWI9ljOlVdz4JSP6Swfb5MuTwLKP2fzfbHEhBQ7dh995MtTs6v7hdtxIbcSFC3dwysjCh4jmaqVMXYqSqiRBMmioVmyHzrZZTeZ40rhS3neY5gOOfxkx88ROA6/MbxV3jlei3tQXmpHqDmd79xuneNDzDAdjAITG8R28lStovAdd5yg/hGGVGGzUga/QHyR37/CWph3JuvAruwdlJzw4xGrDFcqbW4UmuvopDfDPQLziZrSncbWbhvB4mWKC0peXrjrMjTdGNhszS5kqVVOw6nFsqrVNqfvzLUG5TtDwYC05P7ycp32WjRWqg+Bt3az1brCj64p8pEMcZzdO9YZhs+joRW6PDF0xO9ILjWHkRpsdqVOHKYWSjy7OsTtOKVg/GkpugrfMeQ9xK6sTUQSbTge7NVNIaCK5ieCJDCZbkTUfAd7t450hueDRPFE6evUevG66oHtW7ME6ev3XLMvEF/6dbFIDC9RWw3S3k7sTbQbJYR3YikkWjTeyxMFN+9skyiTU9Bux/ZMKgQECrT9+h6yL5nHGEX4u1mVZspSQgMYSLoJtvvM21nu2BVHYyxZbNCmjUVPMWVWsDjZ8dYZ0EfOby2WOD52TIgqHW9XpaV0d47scQ3uue3lLEBhYB25Kw7H1lm9tjBRe6datBVEi/1hCr5lsJe7bicTB2K12ZE/fTytSSO+3bWQcBXz4wjMHzy0CLT4y0KvqIVCcYLIa606hjaSDqxlZ2qJj6R9tk7ItldKeA7kn/94x8mVqZ37cw1OsTKrJJyAlsinG92B8y8AbaNQWC6CdhOlvJ2QGnNF04ucvY7T60LNBtlXxuRNP79K5d56ux1DLYsWcn77B7O0+jGGwalDNpsr5dkgMCVlAPPsve6MUpvLzStL5etzA11E4efeeDKOvXxN7ddi35Vh+WOpNpZIRJkduTrLOg9xaMHlvjIviqd2FlFLc+ylw/uqXLvVJ2RfELR0z2h2WrXoR2v/hPM+kJASsJIjy2z1jAg/YQT5ys8cXZ0U4sRR5pNSRzTYy2ePDfKxw4scc9UHSEkWgt2D0WWPSgEWktcCQVfYQx843KFpK8seWahzt/742dpRTFjxYCHbptgopTn/HILveb79aRgopS7ZZl5A9x6GASmm4Cb1SN6o/jciZOcuFxneGhoSzbgZiSN2VqbV65FHJuqMNfs8tp8nTBRhFuQGDZ7bu3duwCmyjl+/qGjaG34v797gW9fXt72Z+wnLewesoOt7VTFYCP18Tez3awMt1bVwUCvhFj0FcO5eN1iP5JPKPrKzoKl9Pb+Y6p2JV89Y4/tzokmuTSovjpf4mtnxvjEwaUN+0LDuYSSryjn7KyU0qkJpACEwXcMnzi4Nkiu7PtbV4ZvSOJY+Swwkosoeoqcpy05wxh01hczgkRLjp8f5eikIu+5XK62qXdjIqV6190XT80ynPMYznkstcKeVJEBhnMenzg8dUuV8Qa4tTEITDcRN6NHtF28UTbgRiQNbQzVjh2kfX25SbUTEyba+uhs4xj6g9CG/YzFIt+6PMVPPHg7v5hmj0+8dnVDIsBmyEgLT1+s8HMfuIQnV96bKSncvaPO0xcrdJPtX84bWbZDv6rDarTS5/sX+8zsMBtMzUgM/dRyO4O0wP6RDnlP00mDUma1sZFtPNhyYyuS7CitmClmfSptJAdGOxjNhhnR0fEWT1+s3FBQN/ssWeBzpEjt6e1WksSlnQQ0QoUxGkdEnF5oMJzzqHYiRgurTQOlsHqPP/Ohw3z+hXNcq3cAwa5yjp/84KFbTsR10F+6tTEITO9SZIFmI2zEBuwnaVjat0FjenNJja4NSrG25IZ+ttZabNRn0QaKfoJBrty9TzWYKAb82mP34EhJmChcRzJS8GmGCdEbIEsUPEPOMWlQsCW94VzcK2/9d4+c48SFEb52dvwNl/X6CRQ3EqGNEp9m7JB3DQbDaCEm7+s0s4ChIGEpHbLNfJPev7vGSD5BGUk7lsRacu9UA21sUNqMMZdoyflqnsPj7TXHYk0VS74Vfw3VRsoYipxrbvhZmqFPK5bsKGmEkORcS2XRxqARuI6i07FD1J3YJdE+CMNyOwKsPmOYaLy0oRQrQzdR/MSDB/nFR+5kttYGAbuGCoNMaYA3jEFgepciCzRXO+uVvjdiAwauw8MHd/Avn52h1o2JlMYRglgpKvkcy52YROveEiZukNasVUbIe5pdQ920xLayUGpg73AdpRPA6Q0Aj+R8EmUXwWQb/SYpVggBOU8zkk8Yya2YBdpyUcLHbl/qZSFvJCvrx0YlvtcWC3zryhDdxDAzb21BbL9I9QaTtBG9odfljkfBUxybaDAUWA8qKQzl1KtqueNxNB2uPTLW3lD2CODLp8d5/+46QzmVDhevKEx0E4EwrFNjh5VzdSNBXYHgQjXP4bE2IMEIpCNBK0BaqSOtkUJwvVXh3t0TJMogJXz3yhKvXLXkmOz7c6Uk50m+8PxZfvljxzgwVn4TZ3+AASwGgeldiowN+IX5pVWPa2N46MDkxr0uYzAGOpEiVHaRFALq3QhHyh7TzhiDJyVoq3zdj41cah1pmVgFV1HF7T0nsIv1tXqV28d39IKpn0aU2Xp7y8BkpW8EiXY4vVjk3qkaJc8OrK58ZpFq0mnuGG9y/A3MN62FIyRfOzvO8fOjlPyED+ypcWSszft2Nnp065eulfjkIXveE20DZ9zzatLUu1bgVUjZs48HekOx1Q7sHgopBYk1XNxA9ggg1g5PXxzhfTvrSElPYUJgODW/eqh25XytHqrdrFRogC+9Ns6Du+oM5xWx1hQ8H1da2xBXKxABi+1Rrrf22XkrV3Cp2iJS9gYm1lY2CwFKGyaKAV88NYvrDGaWBnhrGIxiv4vx2Yfv4OE9Q5QCl0gpir7LcM7j6XPX+bt/9BR/94+e4jeOv4LSdlDyxLk5pLDmdUXfpeS7FD3XlmRklmPYoBS4EleKniOrTP9t5FKbmeo50vSsJMAGOW0CRgplW9oBHjowSTfR7K4UuHvXEJVc3HOn3agAlx2WEPDMxQlmFkoIYXoZkUo9lcAGyHJOMZIz5Fy54fY2gsB6UX3ktnEe2DuKJwXaSN6/u869Uw1y6cyTDR4NfMcw3/KYbQRcrOZY7npoTarXYDi7XKATO6vMBTM4wuCmAq9rn1vr1gs2g/v21SGaoYPsc5p9/OwYj58d29CFNsuUXKmppArp/U7DGWJt5Y+u1X0u1QJm6zkqhR0M5yc5uvPDvDT/ADOL+1eGhdOeZCnwGC0EJNp6VdleH+wazvd6nJkk0a2IQX/p1scgY3oXw5GSz9wxxl333sdiK+QLz5/li6dmN9Ts+9H7DzDf7LLcjZBrpGE81+GuqQoCeHF2iURDnJZxsimbbKlsbTADZLB9j4JrVi22xmi+PRvw0Oe+Rt6VtJVCGmjHCe/bdZWDow0euy2mkWYij59ZT/tWWcffQIjgLy/t4tCYtREXqTfISpCStCKXBB+1DQKHmwbpsVKOpVbIy1er9nFHMJH3ODqxnm4tpODASIdW7BC4dt/LHUkVKynUCh3+46kJfuaBK5ZB2CcjBHZ+yRGaSK0nLsD6weEbESRg44xIYDalka/tVz19cZLAdTg02gBCLi5HfPTgvdyx+yEevv3kqvm8WBmiROE5klo3RulMvULiSkmirQ/UQE18gLeKQcb0HkDgOowVA565ML8pS68UuJQDj3htbQ47Z9JJFH/w4x/mn3zsGFOlACkg77vsGymxb6SILwU5V5LzfGZSdYN+VDsupxdtppDdvb90bYgvnx7h9EKdl65VOTPf4PxSix/ae50HdjUpB4BwKHiG+6bqPHZwcfWxs5JFSaAcuHiuzyvXi7Tj9NLNFNQxdBPBbL1opZM854YXtyMEhcBFSslsrUMn0bRiS5PXBhpRy/aQ1sAYa7txYTlP5gebSrWitODkQolu4jKzaM/Rcsd6PWltFRYcaSh51hZjJB+zthO21sBvq6wHMgLHynNZD3B1pmfPr58qdvhSUApcPEdy/PwEv//CXv71i7fx709Nc2jqI0gh+ezDd/Ajx/b0MvJK3iPvuyRKY4zNkmyZ1faaMiLErawmPsC7A4OM6T2CtXTwTHDVc2RPWfyxw1N8+8rSqgFIA1TyPuPFgMlyns8+cidPnZtjvJTDS3tBL1+rUvBdpLSq4icuTAAwvUFT3aoTKGLlESlhlauFQGnbh9ImYaK4TCuy9uYY29MCwfR4iyfT/pAAPFeSd5104dPUw4RqJ+ZaYwwh4KO3VSl6MQZL0461w5HxBruHO5xdLPGVM6NoA64E13HQJsGXMa3YwXM8JFakFuydv9KGRNv/V3pjdQhtDN3Y4fGz4ySmxsHRBiVvhR7/+NkxAkfw3KUdYOw5qgtIPIOvBEttH4PoufMCPbuM/v7QRszH7QwTb9QDtN+zLRM+dX4UgyTShmiVjJagGYEUDeYaXfaOFNfN55UCl4/85pe4kspSuUJaZqXIvsN3VvFkgPcuBoHpPYKMWNDoxquM/jxHsKOco5L3+OWPHePZC/M8e36OxNhMqZL32TVc6C0mmUtttrB04oQo0b3gECVNlLElpCf7XF5rXY9M2brelUwN5Zhrhj0JHrA/C4Gi5ClUducv7H8MZlUZy2AFQMNkdWCw7HDJl06Pc/zCGJNFzf075zky1sYgEcLalh/bUUMIOLWwjyjqcs+uOW4fa1LJaUbyZd637xj/zZdinr+8TFevOLNmyNQhHtjVWEUAERhOLhTpKoevnh7FdUYoeIpGaKngjgDfkWgEX+07R2tFY7NglHM0rtA0I3eVgd9GzMcHd9eQwvCVMxObXgf9+oL9EOlzQznNQnsT+wvgSq3Nz//ZX/LnP/WxHsU/I9IstkKG8j6h0iy3Q7JiqTFW8PXScouf+dCtN7M0wLsPg8D0HkHG0vvtZ2d6k/dOmgWEseJ3/+I1funRY/y7//JRfv34qzz+2jWaUcx4cbV8Ui/AhQmXqy2W2yGtyN5ZC0FvEBM0Hz+wxPTEyh39mcUiXz09hutI6t2ERJuUVWePUbOiU5fPaM59i/7aMtZGMH1vCZWgHnrsG45Sg700CAr7miPjLX7o4AHuyL1MFLgIMULgSqQQXK2+xu0jkpeuDtOJN97XE2fHyLuSAyNNCl7SY+U9fXECTwpCpTFK0NReT+xWGWj0GSTeSDR2uePhSs0XXtrFbCNYJRDbn/WM5OOeH9XEIVvuzAZ012IzHUCTPreV6K0BvnFxnv/5ay8RG3j+0iLVTkQl73P/7lEqOY+c6/Rm4fKeIEysA+98q8vnv30e33W2ZZI5wACb4fsSmKanp3cAp4C/OTMzc3x6evpe4F9g/y5empmZ+bn0db8C/J308X82MzPzxe/H8b5b8LMfPsIfPHcG15HE2vQyoj2V4io1iF9+9Cg//aHdtEKHiXJxVdklC3C/8+wMi+kwJaTjOsZmMb4refi2Oe7baXXWlJEUPMO9Uza7ePrcOEk6PNsTfCWT+LGZyPtS9W3L3LPMrpnF4komtQF6QS7doDGQcxWlQKGNtPp9qSWHAApewguvX8LfOcuR0hBCCIwxXFpusdyJGMkptC5tSpLQCB4/N4HnjDMUaOZbBnDwHUkjLYMpA8pstgWLG4nGNkN3VVCC1VmPLfmpXkD2HM37d1fJuYovvjZJvOZ8baYDuF1vJoNVA/+fnngZ15H4rtML+CfOXqfgu5R8h1po1SK6iU4zc0uAuN7o8ucvXwK2Nskc4N2Pt2st/35lTP8rcK7v//934B/PzMx8a3p6+k+mp6f/CvbD/hjwQ8Aw8PT09PRXZmZmbl0e6vcZ1U7McN5nvJTrlfEyMsRSO2Sh2aHa/DbX6ufoxm08p0Apt5f79z9M3ltxx/3ZDx/h9795BkcK4rSfkNHGY6Uo+Q7HJttIKSl4biplY2jHimM7OnzrkqEerb9jz1QOnrowghRwdMIOl9ZDycx8kSfPjQgWk2YAACAASURBVN2QSWePQ1DyXeqhFZmdbxmakWQoEOQ8h0QphJBIKUmUQztWtFSXy1WX3ZUCF5daVDsRQsBwTlPOaerR5j0bYzQCS2AwJAigESZbHmf/8280WKxklZqCt8Iu9KTGEbB3OGLv8CIP7mrw9MURvpb2nbLzm9nEbzRYux1oA11lEErRjlVvvk0gCFVIlDiEyuA7EmVsHzNztY2VQWmzbZPMAd71eFvW8nc8ME1PT38caADfS//fBw7MzMx8K33JfwQeA3YCX5qZmYmA+enp6YvAndn7fhCxmY9Shn7ZocBdvdgO53zOzv0ljc4ZpBDM1kKq3QaxmuXPXrrI3tEP8rMfPpLq5SkqOZ+JYo4wUZyer1ulcWNIDBybCqjkNWFCLyiBfX440BR9TSN2e80lS1+29grlQNFJPM4slvjCd28j72nmm5q8F5DzDZ3IUqsz9fJ+aSQpBV7KJqt3YwSWIj4zX+SRA4uUfJMO3goi5fBabQ+dJCCMXa42Oix1IhrdGCHAlQIhAnJuAehses53DRX40fcd4PiZ63znyhLdWG1JQ9/o+RupMGTwHZESMGwge3B3DUfY8+DJlAXXt82RQsKjty9COk20lijxL7+1h6KvNzQI3A6ybJf0pyOsw26iDY4UHBwvc2ahucrmwnNEj3BzK1LGBzNMNw9v51r+jgam9MD/KfDXsZEVYBzol5uew36QRWB+g8dvGJhefvnlm3W4m+KFF1542/fRD6UN/3ZmiReut6hFCcO+ywM7ivzY9CiOFKuO52CQcGK+3suUDHC9FZFzDV87eYFC2ttRqVK1MVByrvO5r3+H/+P498i5kkrgcK0VU/IdPEdScKAaKhuYtOGl2YT7JgU5z9AII2DFDiGKJYttsUrR4bGDi9yXZgvaCAqe5q4dVRKl0l6JJFIrjZ7+CaQsOGXnIdGGyzUbSGTvNRkjDBxj0IDWmla3y3K9xYxf5OBYFbSlORtj57TOLAb4W5ThTBLyoWKX+jC8eGl74rYbbmeLeSRglS3942fHkGlPyXOsVUZ2PrKfxhhKnuah/cu0IhfNepXxN6q6fiNkJJBObG8VOu020ujed22MoRK4tJpN8q7k4swrXHXeWp/pZv+tvdN/u+9VvN1r+dsWmKanp38a+Ok1D38J+FczMzPV6enpzd66WV1lW4P8d911F0Hw9s1QvPDCCzzwwANv2/Y3wm8cf4UXawY3X2Qsbx97sWbY28rzcLm76njuu3/FDHCpHVJtR/iB4PC4SzkAhUMrTFYICQYkMS0dUevaxvbrzYQwUQhihnIelULAZABXU2FOIT0uVsvctWMZZRxcKQk8hzBJmJkfJkqFRQXgSM3RiRZgKdCu41DwXDpxwtHJDt+8IgiVtCUgY3rOuCXfoRkplDFWuToNSlKkx5wef8GFOye71MMc9dAwlHPoxJpEw1ipweVWxKnFET55BO7b2cWlSytyObNY4tnXJzkyWcKthZtLIzkBH/3AA9x/b8JzS1/nSq3NXLOL0tvT4suON8Na0dh+mFW/Cx4/O4EAHtxdY6oU4rurX+y7Bokh8DTLHVhse6ve/0bt59diM4FZKe33sXusgvQ6XKq18RzJSM5nT6WAAX7k2B5+6ANvrcf0Rv7WwjDc1k3pO/23+27AVufu+7GWv22BaWZm5veA3+t/bHp6+lnAmZ6e/ofAQeADwI8D/cXv3cBs+m96g8d/oLCVvcWH7l4tltk/ezJbb/OP/t036cSKSGtC5eHIxDbr9Yo9eiOS1LoSpe3sU6x1bzGtdWOaUcKBkSL7R0p04phjE5fZO9yk5Ct8NyZOJG1V4mpjkm9fHUOKdu/uuhzYRr7SAoSg4DncMTXMyWtVpExAxHRi15bXhCTnOty1cwTPEVTbEXnf5cJSg2uNLrKPep4Fp8CLKXgJ2thxXG0kkVIIBAUvIXASFhQ8fWGSRlxhoVnl7GJMqATGJJy6XuvN4GyE2UaHH//8CSSC84tNGlFCsp7DsCEEVtxU9Z3P/j05QmxBnBA8fnYcA3zq0AKeo3sNrOxyEMKSUoqeQudXaOjw5u3nV89QJXQTucqqwxhDznPwXYcT/+hT/OaJkzx/aYlaN6Kc894Rk8w3C+e//vygnPcG8f1Yy9/RUt7MzMxHst+np6f/EPjDmZmZ705PT5+anp5+aGZm5hngbwG/BbwG/PL09PQ/xaaIu4FX38njvRWwkY9ShqV2SC3cuIYfuA6B4/RmkrSRXGtWmChc7y3EVlLGCoIqbWeHIpWaxWEX0bznIBCpTFHCsYnL3LWjhkGw2PF6WdH3rvs8dWEIR8RM5h1aWtKIYtppI7/g2cxnpBBgjCVKNENBM3R6pnKx1pg4m50SNKOYi9UWtXa0oQWHNlBPXWBzrsZ3LBXcT1mJ7dglVC6BVCkjUNAMfRKVYLRBAdXuJlxxbODzHIeT1+sst0MbtLeISv3Eh+x8AozkPEsQCROiDWJRRjCRQNz/vJAcP78DV8LDty1S8BV+eikIIZDZkCsrArHZ2zdSkdisjNgPO0NVo5JXFFyFIw37K10Ojbb5nW/uw3Ucpoby1LoRnVjzP376/i37nwO8t/B2r+W3yhzTLwK/Oz09LYHnZmZmHgeYnp7+V8AJ7N/az83MzGzfwOc9gn5Cw1qMFgKGg80XgbXvffzsGEfH2xyoNGwDPpbMLJRSNYGVRbU/N7OBR7DQ6uK7cGisueoVGQX8wEiLv7iksep6gpGCz2jBZ7kdcX65zN076gznPfZUClxabhErxavzZSIlEMLgOYaCl9AIHWbmahhjNeu0gRvRMGMtOTWf58HdLSZKOZY7IZ4j8R2Yb41xx45Rzs0tU4sN3ThhttEh1mZTr6nsM0uZCcNCvRv3Bn7f7AXYjBLKgUvgSlSyotqenV+tU/Fcz2ocqpT6DrBjKM/Jhf2MFHLcNzVH4HRT0omk1vXoJMqa/Ql6ZoVvVkXCT2eoKnnV0/gzCKSEw+NtPn14kZnF29hbKVIO3J700DtpkjnALY2bspZ/3wLTzMzMT/b9/irw0Q1e81vYiPsDi2yuqF9ME1akX3ynu633Aix3Yp6+MMlX4wqlQNOOJVEie8Fo7d2+hL59CoZzqWqDkaztsJQ8Rd5V5Lw8w46mi8P+0RLLnYi2GqFSXOLuHV1eX17kakPz0rUhnjw3hsTw8dtXL5qnF4t87cwYJtmePvhXTo8xWsjhuy3CJKYVuZxdLPHy9VEmhxTjeZehgsdCM6S7jTqcwRItspyiEcYofWOr+f73wvrsJNbGzpa5DkOuQ7MbU8571uNIQKgUnpRobXClLc9lNO285/C3793PLz3yV2l02rx85c+4Uq0zWw+JlenZv+cca7fRjZ0bqkjciByxoyTSoKRtJmtWPpMj4O4dLbomj9ID6aEBLN6OtfxWyZgGuAGyen1GaBgtrKg1fOfFFzd8T1Za+dkPHwHgy6euEiUaz5WUZECiDYFjacitKKVAp5EpW4h8R9ihVGDnUJ69wwHt+AKBuz6HacYOeS/P7kqRZqPBUN7n1z5xF18+NcuLV5b481crnLjgcr1RougX+d61Bq5UPHL7de6dWr1o3jNl6emPnx3fMkOR2JLWF18b4YuvDRHImOWuIFISQ4eLtQ55B/aMeNQ3yDq3wka9oc2Q8SA3y06iROMFLkcnyqhuh8rwMAutLq0o4XK1TbNvPipwHTzH9gCv1tr8m+fP8d3ZZR49uINHDxzh+UvP9jkAC6odl+9eK/Pc5cqqUt1W2nlryRFLHch5OVwZoczqiSxlBJ6jaHYb/Gf3HOVv37ufMFGD4DTATccgML0LsFZM80Z1fKVXWHnZax89uIN/+188xI/9m6d7Jm+Znl4nZehVch4IQTOMiZW9a/dcB0cKhnIehybKnF1s4FLg7qk6UlihVZWqNpxZLLHcUWhahGFM3G7w9/74aZpRwkghYE+lyFJbMTOXMFkKGcn5GNPm6ER7w0UzE3TV22CUCSF6i/qyXn1elLGGdpdrLSJlVmn3bYaM8acNoDWOFJuz9vqPA/jEDbKTJ89PoLWh5HvMt9tESjOU86l1Y6QUlAOXVqzQ2hAmim5fHG1EbVyg1omYb43x0qUih8aaFD21KkNaey43086DjckRrVgwW6swUWyiE4VJB2vtd+3hyDztyOWpc3P8+1cu966vgQTRADcTg8D0LsJ26vifO7HiobPWk+nTR3fxH165jBCCvSNFduo8L1+tMloosG+kiDaGSGnmGh0aYcLeSoEd5TyuFFQ7EZPFHK8t7kPISxwcbVLyFN3E4cxiiW9c2gEYZmttlNLsGSlS61p1hoVWCMDu4QK+K1nuRgwHLoGMKbjJthfNDSEgcA2ejNNGv1z7NImGQAi0sXbyGrNOtLUfvrMiIhsbS3S/we57pVDXuUF2MtHiW7OT3LdrBNeROGmJNNGapVbYy/yksD21tYdngCv1DlJK/vTFS5xfHsM5O7IlmeFGckhryRFZL0rKFonSeI7GGGsCGSmPdhzw2kKR12tdhvL5ddfXQIJogJuFQWB6D2Eravn/+RO29JuVBAuey3DeZ+9Isfe6nOuwb6REN1H81t/6AIfGy/zEF57p3Q3vHS3TTO7gm7MRc40aeyqjzNZCpIyIlba9GQETxYCFVoRMx1+X2yG7hwtU8j6Xqy1ipXGlt+1FcyNIDJ86tMzRiRY5J15VNusnaCigFSVoY2V9yjllGXsb9LAElklXCjyMMdy5Y4jzi00M1kY8U6RYCwMUPKvbl6xxphUIhgJNJdB8+MAkXz99rafZt9iOrIqFdbJfJ2fUf1zKGC7XWiTpMdxoJirDG5FDynpRILlUyzFWSCh4CbFyqHWLXG1W+MrpEp4UPe8lWLm+BhJEA9wsDALTewhbUcurnXidv85PfOGZDRl/48WAY1OVDbdpA5/DXFMyWbbZ125ToBUmvDZfR6eeRolWxEr3FL8vLrXYO1LgWqODKwWxkswsFrlvansaco6wpTkJlHMeH91/nbt32PcqvVI2E9im/irWNYZPHlrk6ES7pxR+ar7I18+OpQp4K9AGOlHCwbEyrnRsxpVqwWkdU+jLUvr30YwcmuHqQOtK+5pO4nDv7p382ifu4sXLS7w4X6WtYkswEKkSB5v3skR6XKG6kRvTxtiOHFJ/L8oGR0E99OmqHEZL/q+XbuNqU1HvxuQ8hyvVNnsqhR7V/1aVIFqLwQzTuwODwPQewlbU8o2ovTdi/GXOuP3bNMb0+lOhUpxeqDOS89k1nMd17J10gmC+FaI1GCN6w6DLHevhM14I2D9aIlaa1xY8jLm0LcHRku8ynLOZzIGxIndOnO8xxzKs6k8ZmSpbwMdvX+T+nQ2EkBgkRd/wob0tPCn48umxHski25Q2hj3DOTQC34FIGT51eIHp8VbPbPDUXIGnL07QilcymCw7AYmUkPcdXAHF4CD//Ece5befmeHcUoO5dmIVFMyNy4oZRKogsZY9mWGjGaX+x7aSQ+rvRQmRzXBJKjmfxVaHethGyhw5z8pULbQsGzTLtgeutQPcTAwC03sIW1HLA9dZNwh5I8bfRtu8XG3bRUkIhnN+ql3XZrbRxpUOSlnfoGonIuc5hIkm0RpXWnHPoudy22iRbqIJXIf9o2WeuTDJ188nN1xYEy2p5Dwcx6EZxUgRMlowtGNBuEZYdW1/quRqju3opJmVDUESSICDo02kHMH0ZT+ZYsPzl6sorcl5Dp86PM+9Uy0M0AgFrlTcPVVHYb2RMmQB9e6pDqO+4cjkOAcnjnDP3o/ym0/Z/t9IzscVkGwRlJw+OaPMOsR1BI4QPdr7RjNKMwsFMkfgtczAatfbcF/9vaiMHJJoY12Du5JQ+VRyPuRtZi6EYLkbsdus3OAMyngD3CwMAtN7DJsFml94aJrfOP7KOrbeZx++Y0vGX7bNJ05fY6kT4jqSSt5n93CBV65VUcaQKEPedZgo5Wm1O9Qihe85FH2H4VyeyXIO35EkWvP+fWM8dXYOmUryjBR8rjY09a7EastutNgWeerCOCDYUykiZZ7ljkSbiLXI+lPZtJXnKvJunA6e2ses0Kuh6OtVQSwL54nWVqdPCkBxW6Xe0/PDmNTbCabHWjx5bsUOXgrJE+fGefGqw0dvH+YfPPIxOrFdzL98yqqw+K5DzpM0os3J8J4ERzrESuFKye7hPAKYa4VEyQpd/5OHFnjfrhr0lTM/dvuS7et1/G2LuvZne0JYlXNjNK6E04slEi1YbNvrY7wYUO1ERIlV2/j00V23rATRAO9ODALTewybUct/4/grm7L1funRYzdk/GXb/Bv37ONH//AExcB6MGWurUXfRWnD/pES5ZxHw9HoluL2sXLPMTbDaCHgVz9+F0X/FL/+1EmW2iGJthnMUM4lUppH9s9x95rZJmtKCE9f3MHUUJ5WbHh1rsAdE+GW/al65NAIt0ey6B8wbkcJBsNwLsZ3YmKVDiOv+uyrs7O0ZUQ3hkbk8ff/5Bu8fLVKK1LUw4hS4DGS88kJqN/oizSwq5wDDEN5n51DBQQwe+Za2mcyfOrQPH9tegHPsceSGKh1HfKuncztlyfajqjr42fHEMCxHR3ybkwn8VjojPONS0PpubV09WNTFcuwdBz+9CcfZijn3+iT3FIYaOW9OzAITO9R9Aeardh622VT7RoqsHM43+s3xcraa0dKo7TmzEID35XkpaHg+4DZsKRY8D2ePT9PN1bkXKs0bgxUuwme1Bzqo1w76VCRKyV3TnZ45nXFy1erKG14Ia7QTdSW/an4Tbi6WlEfW2prhA71UJJz9brn+wNbJi0UeJKi79KJFOfr1pgQYeWV2pFCqS7JFgoURsB8K8RzJUJIhnMJida9o3/s4CKPHljCd1dqgZ6AkZxKlStkT54ow1YUfCEET5wb5/lZCYQ0Q4eCH6QK6fa7jLXpmVB++ujOd1VQGuDdg0Fg+gHAVmy97bKp1vabPEeQaE2kFL6UONKWgKqxYm/B4W/etZdnLsyv613VuxHPvb6AFLbRnpXWBHbxLPuKOF1QBXY/viNROsKTMY0wtWRna4+jDP3MtLV9F1gp4fWWeQEmjR2JlpyaXx3YDAZHwMyiDWyOgKHAoxy47BrOU+3GONLObGXMNVfaUqbvOLS39GG2qhtamx7RYLKcQ0qJKxPuGG/2AmWWpRlW+lKxsT9daVDaqndsh4IPUO1oEF6qxweJ0riOBCEQBip5j08cnhqU7wZ42zAITD8A2C5bbzvo72EttEKEAF+KHp0aLHMPAZ995E4++wjreldnFhq0I4UrxbrtNyOHTuLhO3blznsOUgrCRNEMHVqRA1izv4wYsJ15nq2M+tZyENQaVsJGlOuZxSLfvDyFLxN2DxfYMZRnvJjj/t2jfG1m1qqRK9NzePUd25cKE7VKNHctfEeijemzthAsdUJGiz4Sw1CgGMopAtf0WHqkP4XMAqphZznCEQZlBO1YcPzq2A1VxT0hSNK+mjbGuvwCQtqgdHRyiE8cnuLXHrtnQHQY4G3FIDD9AGA7bL3tor+H9cq1Kr/wZ3/JYitiuRv1SjyVwKWS83qZ2Nps7NB4mYJvGXtK28kZKWywSbTk3FKJYztqKA3tWNmimTGcXiyjjewpc4MdmM0CRbwN+aKNglhGklg997TeuG+jwFbJCQ5ODPH0P/wUnVj3gvyLV5aod2M8R6CUTlUkdM9LSQClwLWlvT6NJAHkPIcklY4yxtj3as3J63WbXSYeja5EDKXHmEa57JiNgU4sybkmVYMFsWkYtJ9/R8GlqwW10FrWe45kRzlvJaq0QRj4xOEp/vtP3vuulR46+9/+zbfVRHSAm4dBYPoBwVa08DeKwHU4NlVhspyn4HvsNgVipfEcSavZZKyY2zQTK/ou48WAmbk61tzPZgdCwHDg8fLcHrSBw+NNxguGduTzwlWPr55ZmTeSGB47tMj0WItyoJAyz8xCga/MVOjEhsxlqT8n24yZ7TnSekGtsaLYSB+vP7BJ4OBEmR+/7zZGCjlGWBHPfei2Cb54apbxvEu1U6cdSyRQDGxQM0iiRFFKA3RmzphzHcYLAQibaWbPCSFwpGCsmENrzeVGkaOT7ZXPZTJVdNv7qoUeSx2B0yvlCabH2zx5Xq/LmoZzHuMFl3KpxKvXahhstrQ/nVGKlaGS9/i1x+551walAd5dGASmHxC8ESHY7WJtJpZtb6tM7HMnTlLJ+5QDj2YUkzKwybuSu3ZWOHm9xokLE8ws7ObguM/puS5nl7urAssnDi5y35QdZFVGUvA09+1s4AjBn70yjEhtKnxXknM05RzMNTQCSaxtEPQce3xJn8NswTWUfMVyV7JWd28t8p7k79yzn88+fMc68dzRgs/7dl5h9MAi3bhFzlWAoJs4NCKHmfkiXz8/hpSuJThY6yPGCj7VTkilEDBa8LlcbQO2xzZetGK4GMPMwgE+tLdJ0e/iSHvwSgsaoUveVb1gdCPygyttr64RxZytJuTbCikt9b+SD3rZtefYbGlQvhvgncIgMP2A4WYbum2Uid27Z2jTTKwdxfz+N89wrd6hFSU9KR6AUGnOLTRIlGa8mGNqKM/JuTavrwlK/fI5rhRp+crOFh0Zb1HyKyjtoIzihw8tcqBSp+RraqHk/HKZr7w2klo6WKkhVws6UcwnDy9xdKJN3o2tYeFikcfPjOFK66ckxEpJrBy4/OrH7+JXPn4XwDo6/r7yOcYLCwwHPi2pKAa2Z9aKQBlp54oELIdHmGt0WWqHTJbz7BrK8/pym2o7ouA7FH2rZ7hvpNgTfr1UbTPXCplZ3Mlk8VrKD4REWa5g0WedkKzd9wr5wRVQDjy0MXRjhRSGKFWV31MpcGC0zHLnrWfWAwzwZjAITAO8JWyUib383e9sWvL5X558hcvLbWKlSfrM92TaArHeT5ZI8L2rSyy343UluLVWDsYYS6ZwBIGjqOQNyx3BXz1c49iOJkqBMg45V3HnRJUo0Xzt7DhhotBGM5zz+eFDCxwdt6XFRAvynuH+qQauELy6sM+Kp2pbQsx5kgf3jPHzH7FeV2vp+FJodpSqCAT1MKLsZZxDyHuKWuihtWR6vMWzl2I6iWKinMcAr16v9WxHAkdybP84YR+1XBvDcjfCdySnl/bRjGJ2lmoETkwntrR4geG+nY1NqfGeXMluBVAMXPYWJIViCc+RDOU8Pv+Zh2iGycAqfYDvCwaBaYCbgq0ysTBRzNbbPHdxAc8RtOPVczxZKW2+FZJoTSeOqXXXByVYI5+DwXclShvCWFHrSJbboE3MrqEarUiBAZVyv/u19BItEcbwjz96mNHgGnMNSaRMjzGHENy9s8uZZUPBc4mVZiTv0VWG515f5MO/+WX+qw8e4D+9c5Jap0XJh1B5BE5M4MRoJKBxZGblLnCEwREGg2C8KPj1v34P/+T/nWGh1bUsR0jnhqzKw4Fx3ZshAqtwHieayXIOR0I13MeLVzpcbzV7hAybQYlN57sSbTCJIlKKnCMZK+Rxpe4FoKV2SDNMbnlB1gHeuxgEpgHeVvT3Xq7WOpxdbEA6uLo26FgFbZv9JGqDF6Tol8/xpF1MjbGEhQvLQ4zk87SiGjnHyhDZQGN6RID+XosyhvfvusJcfQlvSBErqIdWWduVgpyriVUHKVxcRyNFF4GLKwWHxy7SaJ3i+KmQHz2WkGhBtVtirlUhVC6eo3Gkg+e4RCodfDUCbQSOhInSEA/s3c1o4TxnFurrim+eI3GF5LE7pnjm/BxL7ZBK3mdfJc9Hb5tnqlQlcCIemHL51hWfJ8+OETiCWFnpoePnR3uBSRu5ilouhaXEu75kT6VAvdEgTBSeIweCrAN83zEITAO8reg3LiwGLo4URInekLxssOxmV0qU1gzlPOrdeBU9PMPjZ8cQAu7b2cWXMd3E5exiieMXxnFETKw8GpFD0dcMBzF5V/UCUz10aEW21Pix2xd5be46lZz1ogpccKUicBPacY5uIql1BQ/tv86h0SYlX9GKXUvW8BKGA402sZVeUorhXAvPSYgSB4OhkssxnBfUuw0SDZ3YpRz4FKTmh26/l4If8ODeMZ45N9dTuQBbnhzJBdTDmM88cDufffiOXqn095/9d9TaC4SJpq4MxkTcN2WHcJ+7vBMhNGGiiLWk1rW6hs0wJjG2ROhKByntMG47VlxcbrLUCjH1BM8RfOS2iQ1nzAYY4J3CIDAN8LZhfe9FUMn7LLRCDBtLHxhgpGDtxocCl3o33uR1gifPTfDNywJfxih8lLa6fLFSdBXMLBT5+IElin6CVZezmYrrGB49sMzXz49ydLzFcschcBxyrvVHynmOXdwVzDYqPHJgibsmG8TaoIwk7yl2lDp0Y4fAtfNXvpP11BStCFxHMpzfx21jXcK4jSNtMBPCpxiU0c2Au3Y/BMCvfvwYf/bSRa43ur1ZsLGcy85hjTEazxG9UmmURDjiOp3Yel2BHcAVQjI91uIvXk/IuS6+I+jEikreZ/9IkTMLDQA6cWKp8coyE5U2XKt3cKQg59rvp9qN+dyJkwNH2gG+bxgEpgHeNiy2QuabYU++SAqrDJ5oQ6MbW7PAvlkhazchmCrnKHgOw3mP+WZIV6l19hDZ8Gk3kbS0S8GzOdho0afaNrQTxVPnR3ho3zI5I/sUECTLHY+j4y2+fbVMOdBI6bLUdbltxCNOumitCFzJo4fv58ce/CD/3/c+z3LbJYkTMCCFVXMo+tqGOyFtQHNtoJocKuNJycfv+E8IvAJh3CbwUt3C9PfvvPhdpLDBrOB7/NQHDvHnL19CKcW+oQvsKC1T9OzM00/9yRmEPMaf/uSj/NaJ7xCFdTS2dJmdFk9KvJxKxVcdCr7DIwd38PnPfISFdsTP/+lzPHN+jiTzdBKszG0JyZ6iy8TIcC9TGjjSDvD9xCAwDfC2QGnNF54/y/mlBt3Y3vVX8j57tyOhdQAAGXpJREFUKkX2jxRpRwm3j5WZa3SYb3XTpr/AcSSuI/nJDx6i0Y04eb2B4wjaUYIyWMp0Wgd0HYEyOh3OFYwVAuuqClS7MQVP00kcmnW3b9DUouhnLrseAjtEutDyaUYGpRXaBMyFO/npD8GeisQRAXFDp0w+gUHiCI1B4KXSPQBSOORcD98NCLwCjnQpBEO989L/ez8yOvaLl06wp7SATpXV857hrokaL13/Hvf/by1q3Q4/drfAdxSutHqBGCsl5Dt5Dk6M8z988n380G0TPYHVcs6WC584fbWn22dhiR6dRHGxoViIqr3v6N3iSDvAexODwDTA24LPnTjJF0/NUvY9wriL0rDQCgHYPVzgQ/vHqXVj9o2WkFJYfx+l2VXO8Tfu2stnH76DRBuev7TEXLPLzFydbtKn9SesSoIxhnt2jvDp6Z385aVFltohRyaHmGt0acfasvdcvWrQFKAVO0QqoBaNM5xfINHW3NCu25LZ+jCvnLsKwD2TRfaOeOwaLvD6cotGGBMrhXQTPBngO6m7LxC4OQCmhm7Hkdv/83Kk5Gc/fIjfePyLNEO5RrFCcGisxZPnq/jClgR3lrs4ErQWdBKHWtflSn2Y8WKRRw+tH4b9++8/yK8/9apl5KWySP3CSxIrwJt9R3fsGB4QIAb4vmEQmAa46YiU7vWW9lTsHfdSJyRWhno34mc+dJh//PBRfvuZGY6fvc6Oco4jE0M8uHeMX/34MQq+dVl1pFUc+POXLxG4kkRZ6SCwbriZdNBfO7aHX/n4XT05oDBR/L0/fprrzS4Xloe4c2IZjS0lGmNwJJxfLjNZLHFqYYJYaSaLy+QCTag8rjcrnFrcl9qCLPCx2w9wtXoKRwoOjJXQxhAlZQp+ASkMy63rKB3jSo/h/CQ7h2/njrR/9EZwrV4FopTsvTqQFj1F3lU8tL9K3kvoxA55V+NITcGF60mOmcW9/PW7dgAwW2uvmkHaO1LkwFjp/2/vzKPkquo8/nmvqrqqq7d0yAYhEMLyQxJBCSiEEMIyCoOiIjDMwMgiDoqiguKZcVTA0RkPCjqg48EBx0FkFD2IqCjITmQRwiIJyQ+SsHRCGjqkl/RW26v5476qVHe6k3Snq6sq5/c5p09X3XrLr17dd3/v3vu73x8buvpJ5ZxGYalGXyrIE2QD4lGfzv4Ui/ebYcN4RsUwx2RMON2p3DZpNjzPqRJkcnn60tmdlkgqDHFt7kvxerYPz/fx8h6RiIfvuwiyy5ceAmxdS5XK5tijIU5Hb4rH2maQC3IcOK2fhliWgVyMDVtaWde5J9kgx6q3elizaSqD2Sb2bokwtaGFfH7r4uDN/SmmNx9LzPdo71nHYGaARKyefac655PPB6Qy/UQjdWRz6eLw3XiY1TwFiOMxsM1nfekI2cDjkBlOoqgnFad7MMDzXDB8POqz5IDppLM5zvrfh4vXdPHc6Zx9+H7MaKrngiP351v3r6AvEwxR3AC3biqdzeEDrU1xzn73fuP6DoYxEZhjMiaclnikmGZjfVc/m/oG8TwnHxTxPR5c005LIrbDzLmwVVni4kUHcc0DK3i6bTOdAyka62KcdOAsLj9+/jYqE/FoBN/z3NyV53Hf2uk88EpAfTRLXSTBZUsPZf5eAb9Zub7oDFe0d/FqZ57e9ABzpjQUjzU1GWdaYz2zpyzh4GBRMXih6Hw8vzhvVBcO442XZF2c1oZ9GMgo2WBrr8kjj25qZFoySmsiYCDrh0EjPp4HjXVRfD/PT59bw8Yej6Z4lDlTkqxq7+axVzv44eMv8849p7Bo7nRmNCXIA90DmWIOLD8MhGiIu/VZ75g5hRlNu/ZdDGNXMMdkTDh1EZ+l+8/kzhVtQxLl5YEp9XVEfX/MUV/JuhhXnfzu4nDdaD2sVDbHG939ZIKA6Y0JNvYMuBxDgc9ANoHnR7hrZRudA2lmNtYX92tN1LGpb5CugTSzW5LF1PGlYrTDAxnKwScWn86Ny+6g7e01RCMZetMR1nU2s37LvuzTGiOVaycezVGHi0qM+m6RcCoTpa0rS38GugbSbOjpJ+L5JGIRtqQydPSl+PULbQykcxw0vZkV7d1kQ1kol6zROcJskOeIOVNtGM+oKJPumETki8C5QAa4RFWfEpHDgB/i2q6/quqnwm2vAM4My69W1bsn215jfHx2yTvoHkyzYmM3ARDzt0blBfk87T39vLJ5C83xujHpsY3WwxqqMNHP2rf7aE3GqIs42aFoePxMNmAgnWNj9wDTGxLFNValc2H96SzTGxMcMWcqFy86aGIuyE4Si0T5zHFn0Z9OsWbTW/SmIizYcxoNdVGuf2QVbZvfZFp9O7FIhJZEnO6BNB55VnUk6UtTTB+SDSDvBfSl3TzSyo1d1EV9Utkce09JOvFa3yOVDYop26MRj1mN9XwpFKY1jO1RzrZ8Uh2TiMwHzgaOAA4FPgQ8BXwP+Fz4xW4TkVOA1eG2RwMtwKMico+q7jAptVF5Ir7Pv5x0KE+3baZrwCXM84D1nX10DqbpT2dZ+v17ScYiLNhzCscf4FJ1jzffT6nCRDIeI53L0daVIRfk8T3wwoVQ+XyeVzt7GczlSGdzJGLuFvA8jzmtDRw4vYnD5+zBs+s3c99L7Ty7obOorj2ZuYiSdXEO3WvOkLLLls5nIHMQz772CL2DbfSletnYk+HNvmncv7YJzxuaoTjIF3pVbgg1CPJkcwGvd/UVe4iJqE8en2QE9p/RwocXzCkGnxjGaJS7LZ/sHtMHgNtVNQs8AzwjInXAfqr6VLjNb4GTgD2BP6hqGugQkdeAQ4AXJtlmY5zEoxFOPHBW0WG0dfaxqW+QVDjznskFdGVzrH6zhy1h2vfxqA0MV5h4o7ufIMgThFFnhQbawyl25/Pg5z3atwwwd2pT8ThBPk8s4rNs3VvF9BW9qSx3rVw/btsmmvpYjEUHnEguyNIzuIVbnl9OR2+WTK4rTM+xNaqhEHMXKVln1RCP0RCLMndqI7l8QH8mRzIWZXaCYpi+YewEZW3LJ9sxzQVyIvJHIAZcDnQAnSXbvIX7Im+Hnw0vN8dUQxQauvtfbmfzQIpIxMcPnAMA11PpHEwzm+S41Qbe7ksVowCDvFuPFI/6kHU5nkpFI+qiPvl8nlnN9SSiPvWxCN2DaaYmXQTbo690DEk/D4Rh49WlhBDxo7QmW1kyb0/uXNFGIpqnJZGjc8BjIEOoa+4CGxLRgtCt09+b2hDn+2e8l3gkQmM8Sm8qy2u6kqPfU3nHa9QMcyljW142xyQiFwEXDSueCfwROAU4BrgJ1wUsZTT1yJ1SlVyxYsUYrBwfy5cvL/s5xkK12QNDbVrSBAccVM+rb/pEfI9XMllyua29+Hw2T2d3D109Hg8+/hTTk2MbSkrnAiKZQbYMBKSDPIOZbChv5JQiBrNO4SAP5IOAxphPkizk4OKD6on5DbTEI3Sn+vhZx9vUjTBkt7k7Py7btsdE/G6LkgH9+7SxaHYHUT/DQDbK611NrNo4g7XdGfAgm80S8T2aYhFaIjn89CDta1aX6Pu5gJVqr0e7ymS0DbsjlWjLy+aYVPUmnLFFRORqYLWq5oFlIjIX50n3KNlsNvBG+CcjlG+XBQsWEI+Xb8X68uXLWbhwYdmOP1aqzR4Y2aYF2Rw3rRmkZzDDxoEuciXid77v0drSTHMixvFHHzmuXsmH+5PctXI9DUCiP0cuyJPP55naEGdz72BB6oDmRB19mSyv9eWoj/mszjVz+bGHEPFdYMC+L/XTm8puc/zGeHTcto3ERP1uK9c/wuGJgCC/R1GVYmqyl71bG9m/cy6bB9IEYQ+1EGl42vy9t+kd1Uo9Go1UKrVDx1PutqFW2dG1q0RbPnmzuY4/AO8HEJGDgTZVzQCrRaSwVP50nCd+ADhVROpEZC/cl3lxku01Joh4NMLS/Z0qwZT6uuLwmhtecppupaHZY+WzS97BafP3pjkRoykew/c9pjUk2GdKA411PoPZHP2ZLBu6++kZdJJCjfEYd6/awPWPrBpiY5DPDzn28LDxaiEXZGnvWYfneUVVigV7TmH+rFZOPsjn9vMW85EFc9ijIU42CGiMRzlt/t42j2RMBGVtyyd1jklVnxCRU0Tk8bDo0+H/zwM3iogPPKmq9wGIyH8Dj+BGYT6lqsE2BzVqhkKD+OCaN1nZ3kV/JksyFuHgmc3FqLyxkAuyQxa8FpQk3toywK1Pr+OhtW/Rm84Q8z3qYxF609lhIqbbzh8VbHho7Zts7k8xNRkvRuVVG6lMP4NhSo0ChaCNTG6AbG5wp9Q1DGOslLstn/R1TKp6JXDlsLIXgWNH2PYG4IZJMs0oM8NliAoT72NtMIN8wKoNy0KJoH4SsSSzmp1EUNT3+NXzr/HE65vYkkqTjEXJ5wMOntHCyvbuMHeRG+TuHkgTtCSHKGnvrFRSNRCPJUnEkmRy6W0+S8Tqi6k2dqSuYRjjoZxtuSk/GJNOaUNZSM0wFlZtWMbrm1eFQ1hRMrk0r292w3H3rtmjGJ5eH4symA3oGMgRiQ4Si/oEJXNbmcBp9+3RsG0q8VpozCN+lFnN84rXooCLOhyburlhVBNWc42aonRepRTP89jQtY5H1qWHhHvHIh4x36crlaElEWNzX6q4b8x3GW2rcf5oZymomJcKzBZ6j4ZRq5hjMmqKkeZVCvSleulL9QJbezq+59FU59OdCZjZmMDHrZvK5AL2aqyv+UWlvuczf+9RBGYNo0axGmzUFNubV2mIN9IQb6RncOi86oxkjGaitCbj+L7HQTOaOWLOVL50woLdRn5nMgRmDWOyMMdk1BTbm1eZ3TqPJfO2zjGVfnbhUQfURECDYRjmmIwaZHvzKgfv5bYpDfc+bO/moghrtQc0GIZhjsmoQbY7r+KxTbj3iuefm1RlcMMwdg1zTEbNsr15lVoI9zYMY2TsMdIwDMOoKswxGYZhGFWFOSbDMAyjqjDHZBiGYVQV5pgMwzCMqsIck2EYhlFVmGMyDMMwqordaR1TBCCd3lZDbaJJpVJlP8dYqDZ7oPpsqjZ7oPpsqjZ7YOdtKrnvR9KamrS2oRbZwbWrCF5+WBrpWmX58uWLgUcrbYdhGBXl2IULFy4rLbC2YafZ5tpVit2px/QULnPiRiBXYVsMw5hcIsCeuHZgONY2bJ/tXbuKsNv0mAzDMIzdAwt+MAzDMKoKc0yGYRhGVWGOyTAMw6gqzDEZhmEYVYU5JsMwDKOq2J3CxSccETkO+CVwoar+Liw7DPghkAf+qqqfCsuvAM4My69W1bsnwb7vAkeF5/ycqk5auKeILAB+A3xXVb8vInOAn+JCTzcC/6iqKRE5B/g8EAA/UtWby2TPNbiQ4CjwH7jQ14rYIyJJ4CfATCAB/BvwfKXsGWZbPbAitOn+StokIktx99fKsOgF4JqJtKmS98h42JX7SkRiuHq3Ly4s/gJVXVeJ77GrWI9pFERkf+By4M/DPvoeroIfA7SIyCkish9wNrAY+ABwnYiUdRV16DQPVNWjgY8D15fzfMPO3QDcgGvYCnwd+IGqHgusAS4Mt/sacBKwFLhMRKaWwZ7jgQXhtTgZ9xtVzB7gg8DTqnoccBZwXYXtKeUrwObwdTXY9LCqLg3/Lp1Imyp5j4yHCbiv/gHoUtXFwDdxD2g1iTmm0dkInA50FwpEpA7Yr+Sp67e4ynE88AdVTatqB/AacEiZ7TsRuBNAVVcBrSIycp7xiScF/C3wRknZUuCu8HXhurwXeEpVu1V1AOfkjymDPY/geqsAXUBDJe1R1V+o6jXh2znA+kraU0BEDsbVy9+HRRW3aQQm0qZK3iPjYVfvqxOBX4fb3sfk/m4TijmmUVDVflUdvkp8GtBZ8v4t3IrpWUDHCOXlZPg5O8KysqOq2fCGKKVBVQvCZpN6XVQ1p6p94duPA3dX0p4CIvIYcBtuyKXi9gDX4kYBClSDTYeIyF0iskxE/maCbarYPTIeJuC+KparagDkw4fpmsPmmAARuQi4aFjxlap6zw529cZYXk4qcc7RqMh1EZEP4RzT+4CXK22Pqi4SkXcBtw4716TbIyIfAx5X1VdEZCznLuc1ehm4GrgdmAc8yNA2aaJtqqZ7ZDyM9XrU7Pe1HhOgqjep6lHD/kZySh3AHiXvZ+O63W8w9EmsUF5Ohp9zL9zwY6XoDSfWoQLXRUTeD/wrcIqqdlfSHhFZGE5ao6rP4RrbLZW8PsCpwIdE5AncQ9hXqfBvpqobwmHPvKquBdpxw20TZVO13SPjYSy/UbE8DITwVLUmJdXNMY0BVc0Aq0VkcVh0OvBH4AHgVBGpE5G9cBXlxTKbcy9wBoCIHA68oapbynzO7XEf8NHw9Udx1+VJ4EgRmSIijbgx7wlXeRaRFuDbwAdUtTCxXzF7gCXAF0LbZgKNFbYHVf07VT1SVY8CbsJF5VXUJhE5R0S+GL6ehYti/J8JtKna7pHxMJbf6F62zrV+ENcDrUlMxHUURORU4ArgYFxPaaOqvk9EDgFuxDn1J1X18nD7S4FzcGGpX1HV+0c+8oTa+C1cIxgAn1bV58t9zvC8C3HzFXOBDLAB991/gguPfg0XqpoRkTNw1zEP3KCqPyuDPf8EXAW8VFJ8Hq4BroQ99cDNuMCHetxw1dPALZWwZwT7rgJeBe6ppE0i0oSbg5sC1OGu07MTaVOl7pHxsKv3VRgJfBNwIC6Q4nxVbZvs7zERmGMyDMMwqgobyjMMwzCqCnNMhmEYRlVhjskwDMOoKswxGYZhGFWFOSbDMAyjqjDlB2NMiMhcQIHHw6IYLoz1ElXtGucxLwIWq+r5IvJz4AuqumGUbRcB7TurmiwiUSCjql5JWRPwOk7gc1NJ+THAj1V1RGmEcJs8EFPV7M6c36gdRqnbj+KEVJcAC1X1m9vZ/1xVvXWE8vOBSKgAPqb6Ey5PSajqMyLyz8ALqvr7He1X65hjMsZDh6ouLbwRkW/jVKu/uKsHVtWzd7DJBcAvgHHL+avqFhG5E6fGXKo4fR7w4/Ee19gtKNZtEUng1hXdpqofxi1uHRERmQ18Eic/NQRV/cku2PMR4E3gGVX91i4cp6Ywx2RMBI8AFwOIyKs4xzFPVc8UkbOAS3G6XR3ARar6tohcAlwCtFEiLxPufxLO8VwPHBF+dC2Qxa1sf4+IXIZLA/BfQBKnrvBlVb1PnBjcrUA/o69+vxn4z/AchUboI8A7w/dfx6k1g1MHPzdU/ijYeRUQVdWvlNqtqmtE5N9xq/HrgYeBL6mqLRisMVR1UEQ+D7wc1tdFqnpuuGj3BNwi1g24B5rbgHeKyC24h5uvAoPAHbiF1sW6AnxZRE4EmoCPqeqKYfVnKfAN3ALaS4FuEenHaUAuU9WbRORCnCPsxzmuT6hqj4h041JenIwTdj1LVV8o42UqCzbHZOwS4Wrz0xkqEfNy6JTm4PTrTgpzxDyEuylbcJI4x6nqKTjV9uGcA8wMJXROBs7Hyf8/hxvqewCXsPFaVT0BOA24KRy6uxI3JHcc8NeR7FbVZUCDuMRshPs/pqrt4TH6gWPV5d2aArx/J6/HmcBsVT1OVd8DHIDL0WXUIOHDyNM4J4KItAKfBo4OcyTdgZNSuhI3zPaxcNcjcEn9RkpouCqsmz/AKZaMdu7Hcb20b6vqbYVyEdkHp5JxYti7awMuCz9uDu04Afg524pT1wTWYzLGw3QReSh87eOc0ndLPn8s/H807qntnlDROg68gmusX1XVt8PtHgTeNewc78U5MsK5q1MBhiljHw80iciV4fsMMAPX6ykkSXtgO9/jx7in3SvC/zeG58uKSA54VESyOFmqkZznSBwPHF1yfVqA/XZyX6M6acFlhEVVO0XkHuBhEfk18AtVXS8i84btoyW6jcP5U/j/McY3/H04sLxE9+8hXO+pQGGU4DXcvVZzmGMyxsOQOaYRKCgap4C/qOqQHoOIHIHTLiswUrbfPDvu0aeA00sDGMLjeyXH314m4VuAv4jIdcChuDxOhSCIC4EjVLVPRH41in2lFPLepHCprr+zA9uNGkBEkriHpv8rlKnqGWHSxVNxDuqjI+y6PVXvQt302FqPSuvTjnIoDa973rCy7LDPag4byjPKyVO4+aCCFP+ZYc6ktcC8UB3ZY+tcTimP4YbwEJFmEXlSXNKzABctBbAMl7ocEZkmIt8Ly1/E9dbAzVeNiKq2A8/g5q9+VhIpNRPXo+sTkX2Bo3C9vVJ6cHMHiMh8XE+tYNPp4XAgIvI1ETlw1CtkVC1h6ojrcT2cICybJyKXqepqVb0WN5R3GEPr5Y4o1PdjgML8T7E+4eavCox03OXAwjC6FFwdf2Inz10TWI/JKBuq+oaIfA74XTh52w+cFw6HfBM3BPgKTuk6OWz324FjxGWBjQDXqWpaRP4E3BhOSn8W+JGI/D3OcXwj3PfrwC3hfM+fGfoEOZybcXNXpWOE9wJfEJFlwErcPMDXRKQ0kOKXwAUi8ihuDmJlWH4HzpE9Fg4HPsMuRBAak05hmDoCtOLqwmeAQrToeuDdIvIXYAsuo/XVuPo3M6yfo4aU44YE54vIJ3HDw+eG5dcCN4vIS7g6W+AB4DvhAxwA4dDhV4H7RCQV2vTl8X/l6sPUxQ3DMIyqwobyDMMwjKrCHJNhGIZRVZhjMgzDMKoKc0yGYRhGVWGOyTAMw6gqzDEZhmEYVYU5JsMwDKOq+H+HoZQYXKSG5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ecdf19b90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.regressor import ResidualsPlot\r\n",
    "from sklearn.linear_model import LinearRegression\r\n",
    "from sklearn.model_selection import train_test_split\r\n",
    "\r\n",
    "# Create training and test sets\r\n",
    "X_train, X_test, y_train, y_test = train_test_split(\r\n",
    "    X, y, test_size=0.1\r\n",
    ")\r\n",
    "\r\n",
    "visualizer = ResidualsPlot(LinearRegression())\r\n",
    "visualizer.fit(X_train, y_train)\r\n",
    "visualizer.score(X_test, y_test)\r\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "残差图显示了预测值（乘客数量）的误差，并允许我们在模型中寻找异方差；例如，目标区域中误差最大的区域。残差的形状可以强烈地告诉我们OLS（普通最小二乘法）受模型组成部分（特征）影响最大的地方。在这种情况下，我们可以看到较低的预测乘客数导致较低的模型误差，反之，较高的预测乘客数导致更高的模型误差。这表明我们的模型在目标的某些区域有更多的噪声，或者两个变量是共线的，这意味着它们随着关系中噪声的变化而注入误差。\n",
    "\n",
    "残差图还显示了模型是如何注入误差的，残差=0处的粗体水平线是没有误差的，该线上方或下方的任何一点都表示误差的大小。例如，大部分残差都是负数，而且由于分数是按实际期望值计算的，这意味着大多数时候期望值都大于实际值；例如，我们的模型主要是猜测的乘客人数多于实际人数。此外，在残差图的右上角有一个非常有趣的边界，这表明模型空间中有一个有趣的效果；可能某些特征在该模型的区域中被强加权。\n",
    "\n",
    "最后利用训练集和测试集对残差进行着色。这有助于我们识别创建训练集和测试拆分时的错误。如果测试误差与训练误差不符，那么我们的模型要么过拟合要么欠拟合。否则，在创建拆分之前对数据集进行无序处理可能是一个错误。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "在生成残差图的同时，我们还通过对测试数据（例如代码片段）对模型进行“评分”来衡量性能的可视化工具visualizer.score(X_test, y_test)。因为我们使用的是线性回归模型，评分包括找到数据的R平方值，这是一种统计指标，用来衡量数据与拟合回归线的接近程度。任何模型的R平方值在预测/测试运行之间可能略有不同，但通常应具有可比性。在我们的例子中，这个模型的R平方值只有0.328，这意味着线性相关性可能不是最适合用来拟合这些数据的。让我们看看是否可以使用正则化来拟合一个更好的模型，同时探索另一个可视化工具。\n",
    "\n",
    "在探索模型族时，首先要考虑的是模型如何变得更加复杂。随着模型复杂度的增加，由于模型变得越来越过拟合并且无法泛化为看不见的数据，因此由于方差引起的误差也随之增加。但是，模型越简单，偏差可能导致的误差就越大。该模型是欠拟合的，因此更容易错过目标。因此，大多数机器学习的目标是创建一个足够复杂的模型，找到偏差和方差之间的中间点。\n",
    "\n",
    "对于线性模型，复杂性来自特征本身以及根据模型分配的权重。因此，线性模型期望获得解释性结果的特征最少。实现此目的的一种技术是正则化，即引入称为alpha的参数，该参数可相互标准化系数的权重并降低复杂度。Alpha与复杂度成反比关系，Alpha越高，模型的复杂度越低，反之亦然。\n",
    "\n",
    "因此，问题就变成了如何选择Alpha。一种技术是使用交叉验证并选择具有最低误差的alpha来拟合许多模型。该AlphaSelection可视化工具可以让你做到这一点，有一个可视化表示，显示了正规化的行为。如上图所示，随着alpha值的增加，误差减小，直到我们选择的值（在本例中为3.181）开始增加。这使我们能够针对偏差/方差折衷，并探索正则化方法之间的关系（例如Ridge与Lasso）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0KbfFxcVx8eJFn9+TMYEuPjGJu/67iG3Hz/Fyh/oMalrD6ZAu47MqIFWdqKpDPYuVgAMpNsd5vgqJSChQADjlq1h8bfv27QwaNIhbb72VL774giFDhrBx48YMnaNMmTLJDbCFChWievXqHD169Kr7HjlyhIULF1423ERUVBS///578rqwsDCKFCly1W158uRJ3maM8Q2Xy8WTk1exYMdRejWoxLvdryxxO83nbwKLyHKgInD7pXWqelFE3gJ2AReACaq6Lb1zXRpN85IaNWoQHx+fvNyiRYurHvfoo48yaNAgAJ599llWr159xT6NGzfmyy+/BGD8+PEMGzaMFStWpBcSsbGxPP300wwdOpQKFSrQp08f6tatS40aNYiOjgbgwQcfJCbmykmd//rXv9KsWbMr1h86dIjNmzdTs2bN5HOk9Pbbb/PUU08RExNDQkIC0dHRbN++nWLFivHSSy+xbds26taty0svvUT+/PmvuS0ll8tFfHw8kZGR6d53TuXPsWeW3XPO9N2Wk4xedxQpno/n6xVk3bq113U+X9yzzxOAqrYUkUbAOBFpqKouTxXQP4DawDlgvmfbNccibtCgAXnz5gUgLi4O4LLpE6/Vn/bSfAChoaFX3S80NDR5nzx58hAUFOTVHAKrV6+mfv363HjjjYC7bv7hhx++7NiJEyeme55LoqOjeeWVV3j11VcJD7+yrnDBggWUKVOGJk2asGrVquS4w8LC2Lp1K//85z9p2LAhb775JuPGjeO55567Yts777yTvC2loKAgwsLCiIhIfxa0nCgyMtJvY88su+ec6Zc/9vP5+s2UL5KfOU93p0LRAtd1vszec2xs7BUfnFPyZSNwBHBMVfer6npPVU9p4BhQF9ilqic8+y4BIoDrGow+rbHsU36KHj58eLrnGThw4DUbYFPasmUL9erVA+Do0aMUKFDgih9Uv379rvpJ/pVXXqFly5bJy/Hx8TzzzDP06NGDLl26XPV6a9euZf78+SxevJjY2FjOnz/Piy++yCuvvELZsmVp2LAh4J517NJEM2XLlr1sW9euXRk5cuQV5y5RogT9+/f36r6NMVe34dAp7hu3lHyhIfz8YIfrfvj7ki9LAG2BKsBzIhIOFAJOeLbtAeqKSH5VvQDcDMz0YSw+ExYWllxX//HHH19WJXXJ+PHj0z2Py+Xi1VdfpXr16jzwwANp7vfCCy/wwgsvALBq1Sq+/vprPvzwQ8D9oN+1axfVq1dn9erV1KjhbnAqXbr0ZdtWrFiRvC2lUqVKMWDAgPRv2hhzVUfOXaDX6AVExyUwaWA7IiqVdDqka/LlewDDgTKeT/czgCeB+0Wkt6oeBf4FLBCRpcA6VV3iw1h8pkePHqxZs4Zbb72VOnXq0KhRI959990MnycyMpKpU6eycuVKevXqRa9evVi0aFHy9ocffjjNRuFLXn/9dV588UV69OjBtm3beOyxx666bcuWLZdtM8ZcvwvxCfT+ZgH7z8TwbvdG9LmxstMhpSvo0py1OVlkZGRVYHd6bQBp8fWcwDlRZu559+7djBgxgvfff99HUfmWP9QNZzW755whKclFv3FLmLRhLwNurs4392TtrF5Z0AZQLSIiYk/q7fYmsEkWHR3NqlWrnA7DGL/z9uyNTNqwl9bVyjDizuaODvCWEZYAjDHmOoxfu5shczZSvWQhJg9qR97Q7JvS8Xr5bQIIDg62YQuyWHR0tL0hbEwGrNhznIcmLqdovjCmDe5IqUL5nA4pQ/x2SsjQ0FAuXLhATEwMISEh1yxyxcfHJ7cZBIqM3LPL5SIxMZGpU6de9YU1Y8yV9pw6T59vFpKQ5GLC/W2pG17U6ZAyzG9LAOCeaP3SS1vXsnPnzmyKKOfIyD0HBQWRJ08eZsyY4cOIjMk9zl2Mo9foBRw7f5HPejehi5R3OqRM8dsSwCWhod7dgje9hXKbjN7zpRfFjDFpS0hM4t5vl7DpyBmeai083lKcDinT/D4BmKwzduxYp0MwJsd76ZdIZm09xK11yvNRz5udDue6+HUVkDHGZKfhy7fx+ZKt1C9blO/7tyHU4Skdr5d/R2+y1Pjx470atsKYQDR322Ge+Wk1pQvlZeqDHSiaA6Z0vF5WBWSSffDBB4B78DpjzJ+2Hj3LXf9dREhQEFMGtadayZwxpeP1sgRgjDHXcOL8RXqOXsDZi/GM7deKltXKOB1SlrEqIGOMSUNsQiJ9/7uInSejeO2WG7gvorrTIWUpSwDGGHMVLpeLxyatZMmuY/RtWIV/dsl93aQtARhjzFX8a8EfjF2ziyaVSvLNPS0JDvaPAd4ywhKAMcak8tP/9vGPmeuoWLQAPz3YngJ5cmdzae68K5Mpy5YtczoEYxy39sBJ7h+/lAJhoUwd3IFyRXLulI7XyxKASVaoUCGnQzDGUQfPxtBr9AIuxCcyZVB7GlUo4XRIPmVVQCbZnj172LNnj9NhGOOIsxfiuP2r+Rw6d4EPbruJng0qOR2Sz1kJwCTr1asXABs2bHA4EmOyV1xCIn3HLGLj4dM83rI2z7ev53RI2cJKAMaYgJaU5OLBCcuZv+MIvRpU4rPeTfxmSsfrZQnAGBPQ/j5jLd+v20PLqqX5rn9rQoID57EYOHdqjDGpfL54Cx8u3EydMkX4+cEO5A8LrFpxSwDGmIA0acNenp+2hnJF8jPz4U6ULJjX6ZCynSUAY0zAWbTzKPd/t5RCecKY/lBHqpQIzC7QgVXeMdf04YcfOh2CMT636fBpen+9ABcweVC7XN/X/1p8lgBEpAAwBggH8gFDVHV6iu2VgO+BPMBaVX3MV7EY79xyyy1Oh2CMTx04E81tX81PHtq5U+1yTofkKF9WAfUA1qhqO+Au4ONU2z8CPlLVpkCiiFT2YSzGmAB35kIct301nwNnY/jg9pty3dDOmeGzEoCqTkyxWAk4cGlBRIKBNsC9nn2f9FUcxnvdunUD4Ndff3U4EmOyVmxCIn2+WcimI2d4uk0dXgiQF73SE+RyuXx6ARFZDlQEblfVjZ514cASYBZwE7BEVf+e1jkiIyOrArt9Gqihf//+AIwbN87hSIzJOkkuF68tO8jcfefoWKkw77aqSEguHNo5HdUiIiL2pF7p80ZgVW0pIo2AcSLSUFVdQBBQAfgM2APMEJHbVHXGtc7VoEED8ubNeFetyMhIIiIiMh68H8vMPefJ457k2l//r+znHBgyes8vTF3D3H3naFO9DL880pl8YSE+jM43Mvtzjo2NZdOmTWlu91kbgIhEeBp6UdX1uJNNac/mE8BeVd2pqonAPKC+r2IxxgSmTxZt5tPFW6gXXpSfHmjvlw9/X/JlI3Bb4AVIrvIphPvBj6omALtEpJZn3whAfRiLMSbATFi3mxenRVKhaAFmPtyJ4gUC70Wv9PgyAQwHyojIEmAG8CRwv4j09mx/DvjG00ZwFvjFh7EYYwLI/O2HGfT9corkC2PGwx2pVLyg0yHlSL7sBXQB6HeN7TuA1r66vsm4O+64w+kQjLluGw+d5o4xiwgCpjzQnhvKFXc6pBzL3gQ2yd544w2nQzDmuuw7Hc1tX83j3MV4xvdvQ4eaZZ0OKUezsYCMMbnCqZhYun81j0PnLvBRzwjublzV6ZByPEsAJtnbb7/N22+/7XQYxmTYxfhEen+9kC1Hz/LXdnV5rp296OUNSwAm2eTJk5k8ebLTYRiTIYlJSQwYv5Slu49xV6MqDL09sN6LuB6WAIwxfsvlcvHXn9cwZeM+2tcIZ8y9rQgOvLd8M+2ajcAiUhF3X/6uQBXP6j24h3D4RFX3+zQ6Y4y5hg8XbOaLZcoN5Yox+YH25A21F70yIs0SgIg8CMzBPQbPHbjf4i0N9MWdBH4TkQeyIUZjjLnCuMhd/G3GWioVK8CMhztRLH8ep0PyO9cqATQAblTV+FTrNwObRWQ48L7PIjPGmDTM0UMMnrCcYvnzMOPhTlQoWsDpkPxSmglAVZ+/9L2I3AZUU9V/i0gNYJeqxgHPp3W88T/ly5d3OgRj0qWnLvD45EWEBAfx84PtqV+2mNMh+a10XwQTkQ+AWrjbAP6N++3eMsDTvg3NZDebB8DkdLtPRvHcwv1ExyUwYUBb2lQPdzokv+ZNL6B2qtoHOAegqkNwj+FvjDHZ5sT5i3T/aj4nLybwaa8m9G1YJf2DzDV5kwAueP51AYhICDaERK40Z84c5syZ43QYxlwhJi6Bv3y9kG3HzzGgbkmealPH6ZByBW8e5MtF5BugvIg8D/QBFvo0KuOIF198EYANGzY4HIkxf0pITKLfuCWs2HucfjdV40nJ53RIuUa6JQBVfRX3cM7zcE/t+LGqvuLrwIwxxuVy8fRPq/nljwN0qlWW0Xe3IDjIXvTKKt40Av9NVd8HfsyGeIwxJtl78zYxcsV2GpYvzo+D2pHHXvTKUt60ATQQkZo+j8QYY1IYs3onr/+6nirFCzL9oY4UyWcvemU1b9oAbgS2iMhJIA73hO4uVa3s08iMMQHr1y0HeWTSCkoUyMPMhztR3l708glvEkAPn0dhjDEea/af5O6xiwkLDubnBztQJ7yo0yHlWt4kgAO4X/5qgrsr6EpV/d6nURlHTJ061ekQTIDbeSKKHqPmcyE+kUkD29KqWhmnQ8rVvGkD+BzoCSiwHbhLRD7zaVTGEVWrVqVq1apOh2EC1IEz0XQZMYdj5y/yee8m/OUGq2X2NW9KAA1UtV2K5X+LyBJfBWScc/78eQAKFSrkcCQm0ByLusCtI+ay51Q0b3VtyOOtxOmQAoI3JYA8IpK8n70JnHu1atWKVq1aOR2GCTBnLsTRbeQ8th47xwvt6/Fq5xucDilgePMgnwH8LiKLPMsdgAm+C8kYEyjOx8Zz+1fzWX/oNI+2qM0Ht99EkL3olW28eRP4HeBJYC/uiWAeVdUPfByXMSaXuzSR+6UhHv7dp6k9/LNZuglARMoBTVX1M1X9HOgpIhV8H5oxJreKT0zi7rGLmb/jCL0aVOKbe1raXL4O8KYN4BvgSIrl/wFf+yYcY0xul5iUxMDxy5i++QCda5fj+wFtCA3x5lFkspo3bQD5VPWHSwuqOlFEHk3vIBEpAIwBwoF8wBBVnX6V/d4DWqhqe2+DNsb4J5fLxeM/rmLi+j20qlqaKYPa2UTuDvImAbhEpCuwCHeJoauX5+4BrFHVoSJSBfcE85clABGpB7QFUs87bBzwyis2yKvxHZfLxYvTIhm9agc3VSzBLw91pGDeMKfDCmjeJICHgeHAJNxvAi8DHknvIFWdmGKxEu43ilP7CHgVeNOLOIyP9evXz+kQTC729uyNfLp4C3XDizLz4U4UzW+DuzktyOVyZegAEQlW1aQM7L8c9zwCt6vqxhTrBwFlcXcpHXOtKqDIyMiqwO4MBWqMyTG+23KSz9YdpUKhMEZ2rkrpAvbJP5tVi4iI2JN6pTfzAQwCCgAjcFcDVRKR91X1P95cVVVbikgjYJyINFRVl4iUAB4AOgNe9yhq0KABefPm9Xb3ZJGRkURERGT4OH+WmXu+//77ARg7dqwvQvI5+znnTCNXbOOzdZupULQAi57sQrWSha/rfP5wz1kts/ccGxvLpk2b0tzuTdP7o8BooDewCagG3J3eQSISISKVAFR1Pe5kU9qzuaPn+yXAT8BNIvKJF7EYH9qwYYNNB2my1HeRu3hi8ipKFczL7Ec7X/fD32QtryaFV9VYoDvwg6f6x5t6o7bACwAiEg4UAk4AqOqPqlpPVZvjTixrVfWvmbkBY0zONHXTfh6YsJwiecOY9UhnG9Y5B/Kq862IfAG0AhaJSAvc3TrTMxwo4xk4bgbut4nvF5HemQ3WGOMf5m47zD1jF5MvNIQZD3eiccUSTodkrsKbXkD34a7y+VxVE0WkKvBYegep6gXc8wikt98eoL0XcRhj/MCy3cfo/c0CgoLgpwfa06Jq6fQPMo5INwGo6mHg0xTLNhmMMeaq1h44ye2j5hObkMTkQe3oVLuc0yGZa7BhnU2ytm3bOh2C8WObj5yh28h5RMXG822/1vSoX8npkEw6LAGYZMOGDXM6BOOndp2M4tYRczkRHcuIO5tz703VnA7JeMGb0UA/TW8fY0zgOng2hi43Bzh3AAAf40lEQVTD53Lo3AU+6hnBQ81rOR2S8ZI3JYBEEekILAfiLq3MyNvAxj/85z/ud/sef/xxhyMx/uL4+Yt0GT6H3afO8+atDXmuXT2nQzIZ4E030IdwD+QWAyR4vmzwtlxo+PDhDB8+3OkwjJ84cyGOriPmsvXYOZ5vV4/XbrGpHP2NN72A7O0NY8xlUk7l+HDzWgztYVM5+iNvxgIqBPwVaIL7DeAVwGeefv7GmABzMT6RPt+4p3K8t3FVvrjDpnL0V95UAX0FFME9GNxXuEfw/MqXQWW1qLhEp0MwJleIT0zinm8XM2/7EXrWr8g397YiJNhm8/JX3jQCh6vqvSmWp4vIQh/Fk+WmbNzHXT8qS8tXo3kVeyPRmMxKTEpi0PfL+OWPA3SqVZbvB7QlzKZy9Gve/PQKeqZ3BEBECuLdWEA5QpF8YbiA79fadALpCQsLIyzMxmk3V3K5XDwxeRUT1u2hZdXS/PRAe/KF2VSO/s6bEsAIYKuIrPEsRwCv+y6krNWuRjiFwoKZ9scBPv1LE6urvIY1a9akv5MJOC6Xi5d+iWTUyh00rmBTOeYm6ZYAVPVr3COB/hf3JO8tVdVvZgwJCwmmdYXC7DsdzfqDp50Oxxi/M2T2Rj5Z5J7K8ddHOlHMpnLMNdIsAYjIj8BDqnpGVfcD+1NtLw58pap9fRzjdWtXsTCz9pxl6qb9NiztNaxfvx6ARo0aORyJySk+WvAHb83eSLUShfjt0c6ULuQ3tb/GC9eqAvoPsFpEfgVm8WcCqAR09XylOyx0TtC8XEHyhgYzddN+3uza0OlwcqyBAwcC2KxgBoAP5m3iHzPXUaFoAWY/1pkKRQukf5DxK2lWAanqPKAx7snYnwUmer6e9ay7SVUXZEeQ16tgWAidapVj4+HT7DoZ5XQ4xuR478zZyD9mrqNSsQIseKIL1W0qx1zpmo3AqhqNey4Avx8QrleDSszccpCpm/bzVxuvxJircrlcvPXbRobM2UiV4gWZ9/gtNo9vLhYwnXh71K9IUJB7nlJjzJVcLhev/7qeIXM2Ur1kIRY80cUe/rlcwCSA8ML5aVmlNMt2H+dYlI1iYUxKLpeLv01fy3vzNlGzVGEWPNGFKiUKOR2W8TFv5gNonB2BZIe/3FCZJJeLaX8ccDoUY3IMl8vFC9PW8OHCzUjpIix4ogsVixV0OiyTDbwpAXzk8yiySe8b3FPUTdqw1+FIcqZRo0YxatQop8Mw2cjlcvHsT7/z2eKt1AsvyvwnulDeevsEDG/eBN7nGftnJZdPCPOGr4LylWolC9O0ckkW7DjC8fMXrU9zKk2aNHE6BJONkpJcPDllFSNXbOeGcsWY/WhnyhTO73RYJht5UwLYDSwALgCJKb780l2NqpKY5GLK//Y5HYoxjklKcvHopJWMXLGdRuWLM/exW+zhH4C8GQriLeBD4BdgGvAvzzq/1PfGKgBMWr/H2UByoBYtWtCiRQunwzA+lpiUxOCJy/l69Q5uqliCOY/fQikrDQckbxqB/wLsAIbjngdgm4h083VgvlKpeEFaVi3Nop3HOHLOegOlFBMTQ0xMjNNhGB9KSExi0PfLGbtmF00qlWT2o50pUSCv02EZh3hTBfQScKOqNlXVm4Gm+NFooFdzV6MqJLlcTN5ojcEmcMQnJjFg/FLGr91Niyql+e3RzhS3h39A86YROE5Vj19aUNVDIhKb3kGeOQTGAOG45w8YoqrTU2zvALyHuz1BcQ88l5Sx8DPnjhur8Nepa5i0YS9Ptq6THZc0xlFxCYnc991SpmzcR+tqZZj+UEcK57MhnQOdNwngvIi8AMzxLN8KeDOgTg9gjaoOFZEqnuOnp9g+EuigqgdEZBLuweVmeh965pUvWoC21cNZvOsoB85EW59nk6vFJiRyz9jFTPvjAO1rhDN1cAcK2Xj+Bu+qgAYDtfhzPoCqnnXXpKoTVXWoZ7ESkPrtqwhVvbTuOFDSi1iyzN2Nq+Jywfdr92TnZY3JVhfjE+k7ZhHTPNM4/vJQR3v4m2RBLpfrmjuISDdV/TWzFxCR5UBF4HZV3XiV7eWAJUAzVT15tXNERkZWxd0dNcuci0uk25RtVCychwndq9tMYcDEiRMBuPvuux2OxGSFiwlJvLJkPysOR9OsbEH+1bYS+UIDZvQXc7lqERERe1Kv9KYK6HkRmaOqCZm5qqq2FJFGwDgRaaiqyRlHRMrg7l76RFoP/5QaNGhA3rwZb7SKjIwkIiLiivU9tl1gysZ9BJetxk0Vs7UA4nNp3fO1ZHT/nCYz9+zv0rrnmLgE/vL1AlYcjqZb3Qr8OLBdrpnD137O3ouNjWXTpk1pbvcmAZwBNovIWi5/E/j+ax0kIhHAMVXdr6rrRSQUKA0c82wvAvwKvKqqs72II8sNiKjOlI37GBe5K9clABO4omPj6fX1AhbsOEqP+hWZeH9b8obmjoe/yVreJIDpXN546622QBXgOREJBwoBJ1Js/wj4RFVnZeLcWaJrnfKUKpiX79fu4YPbIwgLCezi8fPPPw/Axx9/7HAkJrOiLsbTY/R8luw6Ru8bKjO+f2vy2MPfpMGbBFBOVd/PxLmHA6NFZAmQH3gSuF9EzgK/AfcDtUTkIc/+41V1ZCauk2l5QkO4u1FVvlimzNZD3FavYnZePseZN2+e0yGY63DuYhy3fTWf5XuO07dhFcbd1zrgP9SYa/MmATQQkZqquiMjJ1bVC0C/a+ySI95AGXBzdb5Ypny7ZlfAJwDjv85ciKPbyLms3neSextXZcy9rQi1h79JhzcJ4EZgi4icxN0GEAS4VLWyTyPLJjdXKkmdMkWY9sd+TkbHUrJgjshLxnjtVEwsXUfMJfLAKfpHVOfre1oQEmwPf5M+b35LegA1gWZAG6C1599cISgoiAeb1iQ2IYnvInc5HY4xGXImNoFb/jOHyAOnGNSkhj38TYZ485tyBLgdeFxV9wJlgaM+jSqb3d+kBmEhwYxatZ303oswJqc4fv4iT8zby/pDp3m4eS2+usse/iZjvPlt+RKoAXTwLN+E+43gXKN0oXz0alCJP46cZeXeE+kfkEvVrVuXunXrOh2G8cKuk1G0GTaLHWdiebxlbb68oxnBwfYyo8kYbxJAHVV9HogBUNX/AOV9GpUDHmpWE4BRK7c7HIlzJkyYwIQJE5wOw6Rj3YFTtB42i+0nohhYryTD+jS1h7/JFG8SwKU3gF0AIlIQd7fOXKVTrXJUK1GIHzbs4dzFuPQPMMYBc7cdpv2Xv3Hs/EU+792EJxuF2zAmJtO8SQCTRGQeUF1EPgfWA9/5NqzsFxwcxIPNahITl8h3kVk67JDf+PHHH/nxxx+dDsOkYfza3dw+aj5xCUlMGNDWhjI3182bKSH/DfwN+AL3zGD3qOqnvg7MCQ82rUlYSDBfLNOAbAweMmQIQ4YMcToMcxUfL9zMgO+WUiAshF8f6UTfhlWcDsnkAt68B4Cq/g787uNYHFe2SH7ubFiF8Wt3M2/7ETrXLud0SCbAJSW5eHl6JJ8s2kL5IvmZ+UgnbihX3OmwTC5hfcZSebqNu1g9bMlWhyMxgS42IZEB45fyyaIt1A0vyrJnutnD32QpSwCpNK1ciqaVSzJjywF2nfRm4jNjst65i3H0GDWfCev20LJqaRY/dSuVi9vMdSZrWQK4iqda18Hlgi+XqdOhmAB0+FwMHb6YzbztR+hZvyKzH+tMCZu83fiAJYCruLNhFcoWzs/Xq3ZwPjbe6XBMANl2/Byth81i/aHTPNKiFpMGtiN/mFdNdcZkmCWAq8gTGsKjLWpx9mI8364JnPGBFi1axKJFi5wOI2Ct2nuc1p/PYs+paN7q2pAv72hmI3oan7LfrjQ80qI2eUKC+WzxFhKTkpwOJ1sUK1aMYsWKOR1GQJqx+QCdh8/h9IU4RtzZnNduudFe8DI+ZwkgDWWL5Kd/RHW2n4hi6qYDToeTLQ4ePMjBgwedDiPgfL1qB72/WYjLBVMeaMdDzWs5HZIJEJYAruGF9vUICoJ/LdgUEC+Gde/ene7duzsdRsBwuVy8O2cjD/+wgqL5wpjz2C30qF/J6bBMALEEcA11wovSs34lVu87yeJdx5wOx+QiiUlJPDVlNW/M2kCV4gVZ8lRXWlQt7XRYJsBYAkjHSx3qA/De3P85HInJLS7EJ3D32CUMX76NG8sVZ+nTXakTXtTpsEwAsgSQjhZVS9OxZlnmbDvMqr3HnQ7H+LnTMbF0HTGPn/63jw41w1n4ZBfKFy3gdFgmQFkC8MJrXW4E4J05Vgowmbf/dDTtvviNpbuPcWfDKsx4uBNF8+dxOiwTwCwBeKFdjXDaVi/DzC0Hidx/0ulwjB/648gZWg2bxR9HzvJs2zqM79+GvKEhTodlApwlAC+9dou7FPD27I0OR+I77733Hu+9957TYeQ687Ydpu2/f+Pg2RiG3n4TH/W82WbwMjmCvWPupY61ytK6Whmmbz7Ayr3HaV4l9/XYsC6gWcvlcvHp4i28/MtaQoKD+G+/VvSPqO50WMYksxKAl4KCgnineyMAXp2xLiDeCzCZFxOXwIDvlvLitEjCC+djwRNd7OFvchyflQBEpAAwBggH8gFDVHV6iu2dgf8DEoGZqprjp6JqUz2crnXKM2vrIeZsO0wXKe90SFmqZ8+eAEybNs3hSPzb7pNR9B2ziPWHTtOiSml+GNjWevqYHMmXJYAewBpVbQfcBXycavvnwB1AK6CLiNTzYSxZ5t3ujQF4beY6kpJyVylg79697N271+kw/NrcbYdp+unM5NE85z9xiz38TY7lsxKAqk5MsVgJSB5QR0SqA6dUdb9neSbQCdjsq3iySqMKJbi7UVUmrt/DDxv2cE/jak6HZHIAl8vFJ4u28Mp0d33/8Dub87CN6WNyOJ83AovIcqAicHuK1WWBlG9VHQNqpHeuTZs2ZTqOyMjITB+b2j2VQ5myMYjnp6ykUtwJ8oXmzKaUjN5zXFxcpo7LSZyI/WJCEu+sOsTsvecolT+U91tX5Mawc9kWiz//vDLL7jlr+DwBqGpLEWkEjBORhqp6tXoTr/rENWjQgLx5Mz4zUmRkJBERERk+Li0RwF+j8jJ0wR/MP5uX1z0viuUkmbnnPHncLyVl5f9Vdsrqn7M3dp+M4o4xi9hw6Bwtq7rr+8sVyb4qHyfu2Wl2z96LjY295gdnn310FZEIEakEoKrrcSebS30nD+EuBVxSwbPOb/yj8w2EF87H0AWbOHAm2ulwjAMu1fdvOHSaR1vUZt7jt2Trw9+Y6+XLuou2wAsAIhIOFAJOAKjqHqCIiFQVkVDc1UOzfRhLliucL4x3ujUmJi6Rv89Y53Q4WaJnz57JPYFM2lwuFx8t+INuI+dxPjaBEXc258u+zchjb/YaP+PLKqDhwGgRWQLkB54E7heRs6r6E/A48L1n34mqus2HsfjEoCY1+M9yZfza3TzSohZtqoc7HdJ1GTIkx/fEdVx0bDwP/7CSiev3UK5Ifn4c1C5XvhRoAoMvewFdAPpdY/tioIWvrp8dgoODGNanKa2HzeKxSStZ+8LtNr5LLrb7ZBR9vlnExsOnaVW1NBOzub7fmKyWM7uv+JHmVUrzREth67FzfDAv872UcgIbCyhtc/QQTT+dycbDp3msZW3mWn2/yQUsAWSBd7o3okLRArw3bxNbjp51OpxMmzBhAhMmTHA6jBzF5XLx4YI/6P7VfM7HJjDyruZ8cYfV95vcwRJAFiiSLw/D+jQlLjGJxyatzHVvCAeq6Nh4+o1bwivT11K2cD4WPNmFwc3s5S6Te1gCyCK9GlSi9w2VWbr7GP9Zrk6HY67TzhNRtB72Gz+s30urqqX5/a+3WWOvyXUsAWShz3s3oUSBPLz8y1o2HznjdDgmE1wuF1+t3E7jj6ZfVt9ftkh+p0MzJstZAshC5YsWYORdLbiYkMiA75YSm5DodEgmAw6fi6HH6AU8NmklocFBjO3Xyur7Ta5mCSCL9b6hMoOb1WT9odO8/ut6p8PJkNKlS1O6dGBWc/ywfg83/usXft1ykM61y7HxpR7cZ+P3m1zOZgTzgY973czinUf5aOFmbpXydKpdzumQvDJ37lynQ8h2p2JieXrKaias20P+sBD+3acpj7WsTVCQTdlocj8rAfhAobxhfHtfa0KDgxj0/TKORl1wOiRzFb9tPUTDf/3ChHV7aF6lFGtfuJ3HW4k9/E3AsATgI00ql2JIt0YcOneBu/67mDg/aA9YuHAhCxcudDoMn4uOjefJyavo/tU8jp2/yDvdGrHoyVupXbqI06EZk62sCsiHXupQn7UHTjFpw16e/fl3/tO3udMhXdOzzz4LwIYNGxyOxHeW7z7GAxOWs+NEFA3KFmPMva1oXLGE02EZ4whLAD4UFBTE6LtbsO34OUau2E7D8iV4rGVtp8MKSLEJibw9eyND5/+BCxcvtq/HW10bkS/MeviYwGVVQD5WMG8YUx5oT6mCeXn2p9Us2XXU6ZACzsZDp2n+6a+8P28TVYoXZMETXfigR4Q9/E3AswSQDaqWKMQPA9sBcOd/F7HzRJTDEQWGxKQkhs7fRDPPIG4PNa/Juhdu9/thu43JKpYAskm7GuF81rspx8/HcsvwOew/bbOI+dLOE1F0+GI2f5+xjhIF8jJtcAdG3NmCwvnCnA7NmBzDEkA2eqxlbd7u2pC9p6O5Zfgcjpyz7qFZLTYhkU8WbabxR9NZtuc4fRtWYeNLPbitXkWnQzMmx7FG4Gz2j843EB2XwAfz/+DWEXOZ/0QXShbM+ET3vjBp0iSnQ8g0l8vF/H3nuOe3aew6eZ7i+fMw/L7W3Nu4qvXrNyYNlgCyWVBQEO92b0xMfCLDlmyl28i5zHnsFormz+N0aNSu7Z89lFbvO8FL0yJZuvsYocFBPN2mDq/fcmOOSazG5FSWABwQFBTExz1vJjo2ga9X76D7V/OYNrij4w+suLg4APLkcT4ZeWPvqfO8OnMd36/bA0C7ioUZ3r+jvdBljJcsATgkODiI4Xc2IzYxke8id9Py81/55SFnH15NmjQBcv6LYOcuxvH+vE18ungLsQlJ3FSxBB/2vJlCZw7Yw9+YDLBGYAeFBAcz5p5W/L1TA3aciKLV57+yaKe9J5CWhMQkhi/fRu33fuaD+X9QqmA+xtzbilXPdqddDevaaUxGWQnAYcHBQbzTvTE1ShXmsUkruXXEXEbe1Zz7b67hdGg5hsvl4teth3jll0g2Hz1LwTyhvN21IX9tV48CeexX2JjMsr+eHOKBpjWpWqIQfccs4oHvl7PzRBT/7NKQ4ODA7sGy8dBpXvolkrnbDhMcFMTgZjV5q2tDyhUp4HRoxvg9SwA5SIeaZVn2dFd6jJ7PO3P+x/Ldxxl5V3OqlSzsdGjZbs+p8/zf3P/xzeqdJLlcdK5djn/1iODG8sWdDs2YXMMSQA5TJ7woy5/pxuCJy5mx+SANP5zO+7fdxGMta+f60kBSkovZ2w7x5TJl5paDuFxQL7woQ3tE0LVOeevPb0wWswSQA5UulI+pD3bgu7W7ee6n33n6p9X8uHEvX93VghqlfFcaeP7553127ms5HRPLmN93Mnz5NnZ4xklqWrkkj7cS+jWuRmiI9VUwxhd8mgBEZCjQxnOd91R1SoptTwL9gURgjao+58tY/E1QUBD9I6rTuVY5npi8iqmb9tPww194t3tjnmothARn/UNx4MCBWX7Oa1l34BRfLlO+X7ebC/GJ5A0NZmCTGjzRSri5UslsjcWYQOSzBCAiHYAGqtpCREoC64Apnm1FgJeAmqqaICKzRaS5qq70VTz+qmyR/Ewe1I6J6/fwzJTfeX7qGoYv38YL7esx4Obq5A31ryGNYxMS+XHDXv6zbBsr9h4HoFqJQjzWsjYPNK3p+MtwxgQSX5YAFgOrPd+fAQqKSIiqJgJxnq9CInIeKACc8mEsfi0oKIh7GlejY82yvPbresau2cWjk1by5m8beK5tXR5pUYsi+a7/7d3BgwcDMHr06Os+V2r7TkczcsU2Rq3azvHzsQQFQbe6FXiildBVyuf69g1jcqIgl8vl84uIyCNAG1UdkGLdfcAw4AIwQVVfSOv4yMjIqsBuX8fpL47FxDNBTzFl+2liEpIoFBZM31rFuVtKUjJ/5nN6//79ARg3btx1x5iQ5GLTiQusORrN70ej2XA8hiQXFMkTQs8axehTszgVC/vHkBPG5ALVIiIi9qRe6fNGYBHpBQwGuqRYVwT4B1AbOAfMF5GGqnrNMQgaNGhA3rwZryKIjIwkIiIiw8flZN3awKcxsQxfvo3PlmxhzOaTfL/tNJ1qlaOLlKNCwml6t2+RoZ4zl8YAysz/VVKSiw2HTrNgxxHmbT/Mkl3HiI5LACAoCJpVLsVDzWtxT+Oq5A/zza9dbvw5p8fuOTBk9p5jY2PZtGlTmtt93Qh8K/Aq0FVVz6bYVBfYpaonPPstASKAnD0ITQ5TvEBe/t75Bp5rV5cxv+/ky6Xu7pMztxwEoPKyI3SRctxSuzydapWleIGsq193uVzsOBHFvO1HmL/9MAt3HOVkTGzy9rrhRelQsywda5WlfY3wLL22MSZr+LIRuCjwL6Czqqau398D1BWR/Kp6AbgZmOmrWHK7/GGhPN5SeLylsO90NHO2HWLiys2sPX6RUSt3MGrlDoKDgripYgmqlihEeKF8lCmcjzKF3F/hhfNTplA+XC73p/WzF+I4EnWBo1EXORJ1gWOef5OXz19k/5lojkZdTI6hUrEC3F6/Bh1rlaVjzbKUL2pv6hqT0/myBHA3UAr4QUQurZsP/E9VfxKRfwELRCQBWK6qS3wYS8CoXLwgg5vVolHoORo1bkzkgVPM1kPM0cOs2HucNftPpnls4TPuaSpLvDbxmtfIFxpC2SL56NuwCh1rlaVTrbLUKFnYXtQyxs/4LAGo6khg5DW2jwBG+Or6xj3aaNPKpWhauRSv3XIjCYlJnIiO5eh596f6o+cvcvz8RY5GXeTY+YssXV+f+EQXbetVILxQfsoWyUd4ofyEF8lP2cLukkLZwvkonDfMHvbG5AL2JnAACQ0JpmyR/JQtkv/qO9wzNXsDMsY4yt6xN8aYAGUJwCQbNWoUo0aNcjoMY0w2sSogk2zYsGEAPPTQQw5HYozJDlYCMMaYAGUJwBhjApQlAGOMCVCWAIwxJkD5SyNwCEBcXFymTxAbG5v+TrlMRu+5ZMmSmTouJ/Hn2DPL7jkwZOaeUzwzrzpxSLYMB329IiMjWwM2VIQxxmROm4iIiKWpV/pLCeB33FNLHsY9haQxxpj0hQDlcD9Dr+AXJQBjjDFZzxqBjTEmQFkCMMaYAGUJwBhjApQlAGOMCVCWAIwxJkD5SzfQTBORT4DmgAt4VlWv2h0qNxGRobi7zYYC76nqFIdDyhYikh/YBAxR1TEOh+NzInIf8DKQALyhqjMcDsmnRKQQMBYoDuQF3lLV35yNyjdEpAEwFfhEVf8tIpWAb3F36zwMDFDV634bLleXAESkHVBLVVsAg4HPHQ7J50SkA9DAc89dgU8dDik7vQaccjqI7CAiJYF/Aq2B24FezkaULQYBqqodgL7AZ86G4xsiUhAYBsxLsfpt4AtVbQPsAB7Mimvl6gQAdAJ+BlDVLUBxESnibEg+txi40/P9GaCgiFz1NfDcRETqAPWAXP0pOIXOwFxVjVLVw6r6iNMBZYMTQEnP98U9y7lRLNAdOJRiXXtgmuf7X3D//K9bbk8AZYHjKZaPe9blWqqaqKrRnsXBwExVDYS3pz8Cnnc6iGxUFSggItNEZImIdHI6IF9T1QlAZRHZgfuDzosOh+QTqpqgqhdSrS6YosrnGO63e69bbk8AqQU5HUB2EZFeuBPAU07H4msicj+wQlV3Ox1LNgrC/Wm4D+6qkW9EJFf/fotIf2CfqtYEOgL/djgkp2TZzzm3J4BDXP6JvzzuBpRcTURuBV4FuqnqWafjyQa3Ab1EZCXwEPC6iGRJETkHOwos93xa3AlEAaUdjsnXWgG/AajqBqB8IFRvepz3dHIAqMDl1UOZltsTwGzcjUWIyE3AIVWNcjYk3xKRosC/gNtVNSAaRFX1blVtoqrNgVG4ewHNdTouH5sNdBSRYE+DcCFyb534JTuAZgAiUgU4HyDVmwBzgTs8398BzMqKk+bqbqCqulxEIkVkOZAEPOl0TNngbqAU8IOIXFp3v6rucy4kk9VU9aCI/Ais9Kx6WlWTnIwpG4wAvhaRRbifXY85HI9PiEgE7jatqkC8iPQF7gPGiMijwF7gv1lxLRsN1BhjAlRurwIyxhiTBksAxhgToCwBGGNMgLIEYIwxAcoSgDHGBChLAMZ4QUSqisiBdPZ5U0Teya6YjLlelgCMMSZA5eoXwYzJDBEJBoYDdXCPO78K+DjF9jHABaA67kG5xqjqpe0VPS9o1QEWqupTnuF9xwIlgMLAJFX9IJtux5g0WQnAmCsVBzaqaltVbQZ0wT3UQkoVVPVWoC3wmmc4BoCawD3AzcBAz/oywM+ecexbAf8IgGHJjR+wEoAxVzoDVBKRFbjHZi+H+4Ge0mwAVT0jItuAWp71S1U1AUgQkZNAMeAI0EZEHgfigHy4SwPnfH4nxlyDlQCMudI9QBOgjaq2B7ZfZZ+UfztBuKccBff0jKTa9hzuqqRWnvPl6gEJjf+wBGDMlcJxTz2Y4BmYqybuB3hKHQBEpLhnu6Zzvs2q6hKRnkCBq5zPmGxnCcCYK00CWnhGnbwD+BD3fNLFU+xzWkR+BhYB/1TVM9c439fAIBGZD1QDvvN8GeMoGw3UmAzy9AJaqqqjnI7FmOthJQBjjAlQVgIwxpgAZSUAY4wJUJYAjDEmQFkCMMaYAGUJwBhjApQlAGOMCVD/DyaJsHV59xOjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ecdb951d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\r\n",
    "\r\n",
    "from sklearn.linear_model import RidgeCV\r\n",
    "from yellowbrick.regressor import AlphaSelection\r\n",
    "\r\n",
    "alphas = np.logspace(-10, 1, 200)\r\n",
    "visualizer = AlphaSelection(RidgeCV(alphas=alphas))\r\n",
    "visualizer.fit(X, y)\r\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "现在，我们可以训练最终模型并使用可视化工具对其进行PredictionError可视化：\r\n",
    "\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ecda3c210>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\r\n",
    "from yellowbrick.regressor import PredictionError\r\n",
    "\r\n",
    "visualizer = PredictionError(Ridge(alpha=3.181))\r\n",
    "visualizer.fit(X_train, y_train)\r\n",
    "visualizer.score(X_test, y_test)\r\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "预测误差可视化工具将实际值（测量值）与预期值（预测值）相互比较。黑色浅色虚线是表示训练集和测试集零误差的45度线。和残差图一样，这让我们可以看到误差发生的地方和程度。黑色深色虚线，表示实际的误差曲线。可以看到该模型效果不好。因此还需要进一步选择模型。但是简单的Yellowbrick使用就是这些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2 yellowbrick数据集\n",
    "Yellowbrick托管了一些UCI机器学习存储库中的数据集，以展示本文档中使用的示例。这些数据集托管在我们的CDN中，必须下载后才能使用。通常，当用户调用数据加载器功能之一时，例如load_bikeshare()，如果尚未在用户计算机上下载数据，则会自动下载该数据。但是，对于开发和测试，或者如果您知道您将在没有Internet访问的情况下工作，那么一次下载所有数据可能会更容易。\n",
    "\n",
    "下载示例数据集后，可以按以下方式加载和使用它们：\n",
    "```\n",
    "from yellowbrick.datasets import load_bikeshare\n",
    "\n",
    "X, y = load_bikeshare() # returns features and targets for the bikeshare dataset\n",
    "```\n",
    "\n",
    "每个数据集都有一个README.md有关数据源，属性和目标以及其他元数据的详细信息。要访问元数据或更精确地控制您的数据访问，可以直接从加载器返回数据集，如下所示：\n",
    "```\n",
    "dataset = load_bikeshare(return_dataset=True)\n",
    "print(dataset.README)\n",
    "\n",
    "df = dataset.to_dataframe()\n",
    "df.head()\n",
    "```\n",
    "\n",
    "这是Yellowbrick中所有数据集的完整列表，以及与它们最相关的分析任务:\n",
    "+ Bikeshare：适合回归\n",
    "+ Concrete：适合回归\n",
    "+ Credit：适用于分类/聚类\n",
    "+ Energy：适合回归\n",
    "+ Game：适合多类别分类\n",
    "+ Hobbies：适合文本分析/分类\n",
    "+ Mushroom：适合分类/聚类\n",
    "+ Occupancy：适合分类\n",
    "+ Spam：适用于二进制分类\n",
    "+ Walking：适合时间序列分析/聚类\n",
    "+ NFL：适合聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 3 参考\n",
    "[https://www.scikit-yb.org/en/latest/quickstart.html](https://www.scikit-yb.org/en/latest/quickstart.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 1.8.0 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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